https://wiki.aiisc.ai/api.php?action=feedcontributions&feedformat=atom&user=Pramod 2026-10-23T04:51:50Z User contributions MediaWiki 1.26.2 https://wiki.aiisc.ai/index.php?title=PCS&diff=9419 2016-01-04T15:50:18Z

<p>Pramod: /* Related Writings, Talks and Events */</p> <hr /> <div>&lt;span style=&quot;font-size:26pt;color:purple&quot;&gt;Physical-Cyber-Social Computing: An Early 21st Century Approach to &lt;br /&gt;&lt;br /&gt; Computing for Human Experience&lt;/span&gt; &lt;br /&gt;&lt;br /&gt;<br /> <br /> [http://knoesis.org/amit Amit Sheth], [http://knoesis.org/researchers/pramod/ Pramod Anantharam], and [http://knoesis.org/researchers/cory/ Cory Henson]&lt;br /&gt;&lt;br /&gt;<br /> <br /> Visionaries and scientists from the early days of computing and electronic communication have discussed the proper role of technology to improve human experience. Technology now plays an increasingly important role in facilitating and improving personal and social activities and engagements, decision making, interaction with physical and social worlds, generating insights, and just about anything that a human, as an intelligent being, seeks to do. We have used the term Computing for Human Experience (CHE) [1] to capture this essential role of technology in a human centric vision. CHE emphasizes the unobtrusive, supportive, and assistive role of technology in improving human experience, so that technology “takes into account the human world and allows computers themselves to disappear in the background” (Mark Weiser [2]). This can be distinguished from Licklider’s vision of human-computer collaboration [5], Engelbart’s vision of augmenting human intellect [16], and McCarthy’s definition of intelligent machines [11].<br /> <br /> In this article, we present a vision of the future of computing, called physical-cyber-social (PCS) computing. PCS computing is a holistic treatment of data, information, and knowledge from physical, cyber, and social worlds to integrate, understand, correlate, and provide contextually relevant abstractions to humans. PCS computing takes ideas from, but goes significantly beyond, the current progress in cyber-physical systems, socio-technical systems and cyber-social systems to support CHE [3]. We will exemplify future CHE application scenarios in healthcare and traffic management that are supported by (a) a deeper and richer semantic interdependence and interplay between sensors and devices at physical layers, (b) rich technology mediated social interactions, and (c) the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning in order to bridge machine and human perceptions. We will share an example of PCS computing using semantic perception [4], which converts low-level, heterogeneous, multimodal and contextually relevant data into high-level abstractions that can provide insights and assist humans in making complex decisions. The key challenge for PCS computing requires moving away from traditional data processing to multi-tier computation along a data-information-knowledge-wisdom dimension, which supports reasoning to convert data into abstractions that are more familiar, accessible, and understandable by people.<br /> <br /> <br /> <br /> <br /> ==Role of Technology in Human Experience==<br /> Ideas on the ways technology -- including devices, computing and communication -- help humans have taken many forms, including: natural human interfaces and interactions (natural computing [17], gesture computing [18], and intelligence at the interface [19]), ubiquitous computing [2], robotics that have focused on mimicking simple human activities, and current efforts in automating complex human activities such as war. <br /> <br /> These different visions of computing fall within a spectrum between machine-centric computing and human-centric computing. One end of the spectrum that delineates major visions (Figure 1) focuses on making computing more intelligent in order to think and behave like humans, in the vein of Vannever Bush (i.e., Memex [20]). A variety of approaches to creating Artificial Intelligence include recent work in neurocomputing all the way to current discussions of droids with increasingly human-like capabilities. The fundamental premise here is for technology to be as capable as humans, or at least more like humans. This indeed can indirectly serve humans. For CHE, however, we are interested in the other end of the spectrum, where the focus is on developing technology, which directly complements humans and enhances their experiences. Between the two ends of the spectrum occupied by making computing smarter and CHE, the middle ground is occupied by work in advanced Human Computer Interaction (HCI) and augmented human intellect, or Ambient Intelligence. In Ambient Intelligence, the focus has been on making machines surrounding humans behave intelligently, making it more machine-centric. HCI accommodates the human experience of interacting with technology, making it closer to the human-centric vision of CHE.<br /> <br /> [[File:technologies.png|Figure 1: Continuum of the role of technology, from making computers smarter to enhancing natural human experiences.]]<br /> <br /> During the last few decades, and especially in recent years, we have seen accelerated development within each layer of physical, cyber, and social domains that have a strong bearing on human experience. We see a new form of systems developing: the cyber component encompassing computing and communications, and its modern evolution for collective intelligence and physical devices (which have been assisting humans for many years). The role of digital technologies (cyberspace) in impacting the physical [21], intellectual, and social worlds is of particular significance.<br /> <br /> For example, healthcare is suffering from the increasing cost of services and an aging population all over the world. In the U.S., over $30 billion was spent in 2006 for hospital readmissions . Reducing healthcare costs using innovative sensing and analytics to create a sustainable future is a grand challenge. There is a need to provide assisted living for the elderly still in their own homes to minimize costs, enhancing their comfort and overall experience. The full potential of the decreasing cost of sensors that can monitor physiological and physical parameters, low cost networking, and increased use of smart phones is yet to be realized. <br /> <br /> We will consider a healthcare scenario as a motivating example to exemplify the vision of Physical-Cyber-Social (PCS) computing, extending beyond conventional Physical-Cyber systems. For example, consider the case of Ram, an Asian male of age 60 who wants to take care of his health, and yet is aware of the costs. He visits his doctor and receives a blood pressure screening and discovers that his blood pressure is slightly higher than expected: 90 diastolic. Let’s consider two questions that he may have: What is the normal blood pressure of an Asian male of his age? What are the best ways of managing a diastolic blood pressure of 90? To answer these questions, we need access to physiological observations obtained from people of similar characteristics and demographics (Physical). The ethnic, social, cultural, and economic background should be considered for similarity (Social). Moreover, in addition to expert knowledge, the knowledge and experiences of similar people dealing with the same health issue is important (Cyber). Current computing systems such as cyber-physical systems are not capable of answering these questions. We envision that PCS computing will provide answers to such important questions in a holistic manner. <br /> <br /> ==Contemporary Work==<br /> For clarity, we have organized the related work in a progressive path toward PCS computing as shown in Figure 2.<br /> <br /> [[File:pcssystems.png|Figure 2. PCS computing at the heart of physical, cyber, and social worlds]]<br /> <br /> ===Physical-Cyber Systems===<br /> There are a myriad of patient monitoring apps that continuously monitor physical/physiological parameters of a person and report to first responders through the cyber world (e.g. LifeWatch , MedApps , CardioNet , and Intelesense ) . These systems span the physical and cyber worlds. In this case, they monitor physical things, such as physiological parameters of a patient, and deliver information to first responders through cyber infrastructure. However, Ram’s questions cannot be answered by a system that does not understand the social aspects of the question. <br /> <br /> ===Cyber-Social Systems===<br /> Social networks such as Facebook, Twitter, and Myspace fall into the category of cyber-social systems. The actors are in the cyber world, engaging in social activities such as sharing, discussing, and propagating their experiences, opinions, and perceptions. Some of the communities may be specialized, such as healthcare (e.g. PatientsLikeMe, 23andMe) , product reviews (e.g. Amazon, Epinions), and disaster management (e.g. Ushahidi ). Ram has to sift through tens of thousands of documents or discussions to find experiences of similar people. However, the physical component is not addressed and he knows that the answers he finds may not be relevant to him.<br /> <br /> ===Physical-Cyber-Social Systems===<br /> PCS systems involve physical, cyber, and social components, but within current manifestations they are often loosely connected. Quantified Self is an example of a physical-cyber-social system. It involves a community of enthusiasts who monitor health related parameters, such as food intake, exercise, sleep, and other physiological parameters. They analyze these observations to come up with insights that are valuable for their own health, fitness, and overall well-being. These insights are shared through articles, videos, and social events organized in many cities around the world. Ram has collected observations of his blood pressure, which are stored in a database on his computer. He visualizes them and presents them to the Quantified Self community. But, his questions still cannot be answered due to the lack of knowledge of the experiences of other people with a similar condition. <br /> <br /> Observations from these systems are too often stove-piped due to many challenges, such as the semantic integration and sharing of heterogeneous sensor observations, and multiple service providers. Current integration and interaction between physical, cyber, and social worlds is brittle involving limited syntactic interoperability or integration rather than semantic integration. These two challenges have led to significant human involvement in making sense of observations from contextually relevant information from physical, cyber, and social worlds.<br /> <br /> ==Physical-Cyber-Social (PCS) Computing==<br /> The vision of PCS computing extends Licklider’s vision of computing in 1960 [5] by adding social, collective, and personalized computing components.<br /> <br /> Figure 3 depicts PCS computing involving observations, experiences, background knowledge, and perceptions in a goal toward a human centric vision. The observations from the physical world are used to perceive an environment. Our perceptions are strongly influenced by our background knowledge and current observations. By analyzing the observations in the context of our background knowledge, we orient ourselves toward subsequent actions. The decision regarding which action to take is based on our evidence and experiences. The final action is executed and the process repeats in a loop. In Figure 3, the influence of background knowledge on the way we interpret current observations though perceptual inference is shown. Perceptions determine our experience and evolve our background knowledge. Experiences from the social world influence our background knowledge and indirectly shape our perceptions and subsequent experiences. All these interactions are heavily dependent on humans to analyze the connections between physical, cyber, and social worlds. The vision of PCS computing is to consider observations, knowledge, and experiences across PCS layers to provide a more holistic computational framework. <br /> <br /> Physical-Cyber systems can no longer be limited to special purpose embedded systems designed for a single task [22]. Our vision goes beyond the interactions and integration of observations from physical, cyber, and social domains. We envision a deeper semantic integration and interplay between the three layers, as shown in Figure 2. The outer circle in the figure represents systems with shallow integration between physical, cyber, and social systems. PCS computing in the inner circle provides deeper integration, interpretation, and personalization of the physical, cyber, and social worlds.<br /> <br /> [[File:pcscycle.png|Figure 3. Computational Cycle in Physical Cyber Social Computing exemplified with a healthcare example]]<br /> <br /> ==Relationship between PCS and CHE==<br /> John Boyd’s concept of observe, orient, decide, and act (OODA-Loop) [23] provides a useful context to describe PCS computing, involving observations from physical, cyber, and social worlds, and their intelligent processing leading to CHE. In order to show how PCS computing can be used to improve human experience, we first need to be able to speak intelligibly about experience. Experience is a broad term, so we will begin by dividing experience into its component parts. This ontology of human experience is composed of observation (and perception), orientation, decision, and action. These experiential activities are derived from the OODA loop, which will serve as a foundational ontology of human experience. <br /> <br /> * Observe – Observation is the act of examining the environment and gathering data. The environment could be physical, examined through the human senses or machine sensors. The environment could be social, examined through social network technologies such as facebook or twitter. The environment could be cyber, examined through Web technologies such as Wikipedia, Google search, or Wolfram Alpha. <br /> * Orient – Orientation is the act of analyzing the data from the observed environment to form a conceptualization of the situation. That is, integrate and interpret the data, translating it from low-level data into high-level knowledge. Orientation is how we conceptualize and understand a situation, based on “culture, experience, new information, analysis, synthesis, and heritage.”<br /> * Decide – Decision is the act of deliberating and choosing between multiple actions in order to proceed towards a goal. <br /> * Act – Action is the process of engaging in a decided activity. This could involve physical action, such as movement, or gathering additional information through observation, by examining the physical, social, or cyber environment.<br /> We characterize the PCS computing process as an OODA-Loop process and Figure 3 can be viewed as an OODA-loop.<br /> <br /> ==How to resolve the Big Data problem?==<br /> The use of physical, cyber, and social data indeed manifests into a Big Data challenge and opportunity. If the existence of social data leads to a Big Data problem, as with many contemporary social networking systems, with the introduction of physical data, the Big Data problem will explode. The Big Data problem can be divided into the task of managing data with four distinguished characteristics: volume, velocity, variety, and veracity. In our PCS computing approach, semantic computing plays a pivotal role in addressing these challenges. The following sketch describes how semantics-empowered PCS computing will address these challenges:<br /> * The challenge of variety, or heterogeneity of data, is addressed by complementing traditional statistical and information retrieval, lexical, natural language processing techniques with semantic interoperability and integration. The former addresses syntactic and structural/representational interoperability. The latter involves the use of ontologies for semantic descriptions of concepts that data and observations capture, semantic annotation of data and semantic integration or fusion of the data. Semantic services enhanced techniques are also used to model and interact with devices and sensors in the physical world, or agents interfacing with humans.<br /> * The challenge of volume and velocity is addressed by integration and interpretation of data at the source (or as close as possible), through &quot;intelligence at the edge.&quot; This is accomplished by downscaling semantic processing of data and making each resource-constrained device more intelligent, i.e., capable of semantic filtering, integration, and interpretation of streaming heterogeneous data [9].<br /> * The challenge of veracity is addressed by assessing trustworthiness and provenance of observations in the PCS systems. Trustworthiness may have different interpretations in physical, cyber, and social domains. Semantics can play an important role of meaningfully integrating these different notions of trust spanning physical, cyber, and social worlds [24].<br /> <br /> ==PCS Operators==<br /> Besides the semantics-empowered capabilities such as those described above, we expect that the core of PCS computing will be made possible by a series of PCS operators. We describe two such operators as early examples, shown in Figure 4. <br /> <br /> [[File:pcsoperators.png|Figure 4. The DIKW [15] triangle along with two types of operators of PCS computing acting on healthcare related data as an example]]<br /> <br /> ==Horizontal Operators==<br /> Semantic integration of heterogeneous, multi-modal observations play an important role in deeper integration and understanding of observations spanning physical, cyber, and social domains. These operators map multimodal PCS data into concepts to support semantic integration within each level of the multi-tier computation along data-information-knowledge-wisdom dimension. Heterogeneous observations in the domain of healthcare include: (1) machine sensor observations: physical measurements from multiple, heterogeneous, active (active human involvement e.g. blood pressure) and passive (passive human involvement e.g. heart rate) sensors, (2) self-observations (in the form of subjective thoughts, feelings, moods, etc.), (3) social observations (from a network of family, friends, colleagues, etc.), and (4) demographic observations (aggregated characteristics of the population with similar attributes and/or lineage). PCS horizontal operators integrate all these observations (within each layer in Figure 4) using semantics-empowered integration techniques. <br /> <br /> ==Vertical Operators==<br /> Vertical operators are used to transcend observations from a lower level to a higher level in Figure 4. These operators are unique compared to existing operations of ascending through the DIKW triangle in the following two ways: (1) the operator is agnostic of the source of the observations, and (2) the knowledge base is not limited to a formal ontology – it spans from statistical knowledge to social experiences. Semantic perception [4,8,9] is one way of ascending through the triangle.<br /> <br /> ===Semantic Perception===<br /> Perception is the act of translating low-level data, acquired through observation, into high-level knowledge. People have evolved sophisticated mechanisms to perceive their environment, which allow for high-level conceptualizations (or abstractions) of a situation. These abstractions are then used for subsequent decision-making and action, without mentally overwhelming a person through the sheer volume and velocity of incoming sensory signals. Through study in cognitive psychology, scientists now understand that the key to perception is prior knowledge [6][7]. In order to design machines capable of perception, Semantic Web technologies may be utilized to integrate observation data with prior knowledge on the Web (such as Linked Open Data) [4][8][9], in order to interpret and make sense of the data. Traditionally, perception is thought of as the act of abstracting physical sensory signals (i.e., physical data). However, we must expand this notion to envision machine perception capable of utilizing prior knowledge found throughout the Web (cyber) in order to integrate and interpret observations from physical, cyber, and social dimensions. This type of perception is far more encompassing and far-reaching than the capability of any single person.<br /> <br /> “In the next century, planet earth will don an electronic skin. It will use the Internet as a scaffold to support and transmit its sensations. This skin is already being stitched together [10].” With the advance of technologies such as semantic perception, along with PCS computing, this dream is quickly becoming reality. Another concept called the Global Brain [22], envisioning computing to be more encompassing and holistic states that “The best symbiosis of man and computer is where a program learns from humans but notices things they would not.”<br /> <br /> Next we describe two early examples of next generation applications that can be made possible with PCS computing.<br /> <br /> ==Scenarios==<br /> ===Healthcare===<br /> Personalized health and patient empowerment are important themes for the next generation of healthcare technology. Today we have smartphone applications driven by background knowledge, we have an increasing number of cyber-physical systems, such as medical implants for heart related diseases, and we have social media applications for patients to learn from other patients and experts. Nevertheless, medicine and health are complex, with a wide array of variables, including symptoms, vital signs, personal history, family history, medications taken, genetic makeup, diet, exercise regimens, expectations and quality of life choices, social and economic factors, and so on. All these variables need to be put in a social and personal context. As an example, the NIH has identified that a person with diastolic pressure between 86 and 90 may be considered pre-hypertensive, and above 90 classified as stage I hypertension . For a South Asian male such as Ram, however, preferred and normative ranges may be lower. <br /> <br /> Given the rapid growth of the Quantified Self movement and the use of social media for health, it is appropriate to expect that in the future one would have devices taking vital signs with respect to a health concern, and PCS computing technology constantly evaluating health conditions with respect to relevant knowledge (e.g., knowledge derived from the NIH, and other recommendations) as well as social knowledge. For the latter, consider that a South Asian male is likely to have a good cross section of friends with similar genetic and socio-economic profiles, so his health condition can be compared with those of his friends for a more personalized solution. Knowledge that several friends who are close in age, with similar education and socio-economic situations have cardiovascular problems would empower this person to closely compare a broader variety of variables. This leads to a better understanding of his risk profile that wouldn’t be possible without such social knowledge.<br /> <br /> PCS computing would be able to answer Ram’s questions, introduced in the beginning of the article, and as shown in Figure 3. The numbering indicates the ordering of the processes in the cycle. Ram has diastolic blood pressure between 86 and 90 mmHg. The background knowledge from authoritative sources (e.g. NIH) indicates that he is pre-hypertensive. However, knowledge from similar people with the same ailment indicates that Asian males have lower thresholds than average for being diagnosed with hypertension. This background knowledge influences the perception process in inferring a higher risk. PCS computing can provide deeper insights and corrective actions. It is capable of integrating observations, experiences, and knowledge from people with similar ethnic, social, cultural, and economic background. Considering all these aspects while computing solutions, PCS will provide effective, personalized, and actionable information to Ram, as shown in Figure 3. Being an Asian male, it is recommended to reduce his salt intake and substitute it with spices.<br /> <br /> ===Vehicular Traffic===<br /> With an increased demand for resources, cities are under pressure to reduce and optimize resource consumption in a city. Traffic monitoring is part of a broader scope of sustainability applications (e.g. water, energy, public safety) and involves cyber-physical systems. Observations in the domain of traffic span across multiple modalities spanning machine sensors to citizen sensors [12]. Machine sensors include speed sensors, cameras, noise sensors, etc., while citizen sensors include people reporting observations about traffic (such as police, and commuters). All these sensors are monitoring a physical system, the road network, which collectively forms a physical, cyber, and social system. These sensors are interconnected and their observations are available on the web. There are social (textual) observations about various events in a city some of which may influence traffic. PCS computing envisions a holistic approach to computing by considering observations from all these modalities and further exploiting cyber and collective knowledge.<br /> <br /> Figure 5 shows a slow moving traffic event being detected by sensors monitoring the speed of vehicles (red strip) on I-77 South at Ridgewood Road. Current cyber-physical systems [12, 13] do not exploit collective knowledge of the domain of traffic available from existing knowledge bases on the web (e.g. ConceptNet 5 [14], Open Data from city authorities). The knowledge of relationships between events affecting traffic can be derived from these sources. In this example, ConceptNet 5 defines a causal relation between an accident and slow moving traffic. There is a tweet and a news article reporting an accident on Ridgewood Road where the machine sensors detected slow moving traffic. <br /> <br /> Such an event will have a different effect on a person travelling vs. a decision-maker such as traffic authorities. PCS computing will consider the context of this observation by asking questions: Who needs this information? How does this impact a person travelling? How does this impact a decision-maker? How long will these events last? What available knowledge and social experiences can be used in this analysis?<br /> <br /> [[File:traffic.png|Figure 5. A traffic scenario showing physical, cyber, and social observations collectively processed to make sense of a traffic condition.]]<br /> <br /> Considering different modalities for analysis will help us deal with incompleteness (complementary sensor observations) and uncertainty (redundant sensor observations) which prevail in most of the domains making physical-cyber-social observations an important way of dealing with problems in many domains.<br /> <br /> PCS computing involves personalized and contextual processing of observations for enhancing human experience.<br /> <br /> ==Conclusions==<br /> Technologies have been assisting humans to solve problems in many ways. There are many computing theories that exist for solving problems using crisp and well-developed theories. Humans are inundated with a lot of observations from physical, cyber, and social worlds. Yet, we perform integration and interpretation of these observations in a seamless way. <br /> While the approach of computing has been in modeling problems using well-formed theories, we envision two possible ways of achieving seamlessness as humans: (1) Use multiple theories that are well formulated to solve a problem (e.g. probabilistic, logical, statistical), and (2) A fundamentally different approach to computing. <br /> <br /> PCS computing captures a synergetic interaction between computing and humans while providing holistic computational solutions extending physical, cyber, and social worlds. The future of technology will not be primarily about asking questions and receiving relevant documents for search queries. CHE enabled by PCS computing represents a paradigm shift from search-based technology to solution-based technology where knowledge is generated by continuous observation of human activities within the physical, cyber, and social worlds, and to use this knowledge to improve human experience. That is, the vision of PCS computing is to think of computing which translates into action using knowledge from cyber space, an idea that is synergistic to that of the global brain [22].<br /> ==Related Talks and Presentation==<br /> <br /> &lt;html&gt;&lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/JwjPT32ACh2Lbz&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/apsheth/physical-cyber-social-computing&quot; title=&quot;Physical Cyber Social Computing&quot; target=&quot;_blank&quot;&gt;Physical Cyber Social Computing&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/apsheth&quot; target=&quot;_blank&quot;&gt;Amit Sheth&lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;&lt;/html&gt;<br /> <br /> <br /> &lt;html&gt;&lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/Bm7xgNnhlBCYQq&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/apsheth/physical-cyber-social-computing-an-early-21st-century-approach-to-computing-for-human-experience&quot; title=&quot;Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience&quot; target=&quot;_blank&quot;&gt;Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/apsheth&quot; target=&quot;_blank&quot;&gt;Amit Sheth&lt;/a&gt;&lt;/strong&gt; [27]&lt;/div&gt;&lt;/html&gt;<br /> <br /> ==References==<br /> [1] Amit Sheth, 'Computing for Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web,' IEEE Internet Computing (Sp. 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The New York Times (Retrieved 11-28-2012).&lt;br/&gt;<br /> [22] Global Brain, http://www.slideshare.net/timoreilly/towards-a-global-brain-7968429, TEDxSV talk, May 14, 2011 (Retrieved 11-30-2012).&lt;br/&gt;<br /> <br /> [22] White, Jules, et al. &quot;R&amp;D challenges and solutions for mobile cyber-physical applications and supporting Internet services.&quot; Journal of Internet Services and Applications 1.1 (2010): 45-56.<br /> [23] OODA-Loop, http://en.wikipedia.org/wiki/OODA_loop, (Retrieved 11-30-2012).&lt;br/&gt;<br /> [24] Pramod Anantharam, Cory A. Henson, Krishnaprasad Thirunarayan and, Amit P. Sheth, &quot;Trust Model for Semantic Sensor and Social Networks: A Preliminary Report&quot;, Aerospace and Electronics Conference (NAECON), Proceedings of the IEEE 2010 National , vol., no., pp.1-5, 14-16 July 2010.&lt;br/&gt;<br /> [25] Yan Xu, Maribeth Gandy, Sami Deen, Brian Schrank, Kim Spreen, Michael Gorbsky, Timothy White, Evan Barba, Iulian Radu, Jay Bolter, and Blair MacIntyre. 2008. BragFish: exploring physical and social interaction in co-located handheld augmented reality games. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (ACE '08). ACM, New York, NY, USA, 276-283.&lt;br/&gt;<br /> [26] Blake Sawyer, Francis Quek, Wai Choong Wong, Mehul Motani, Sharon Lynn Chu Yew Yee, and Manuel Perez-Quinones. 2012. Using physical-social interactions to support information re-finding. In CHI '12 Extended Abstracts on Human Factors in Computing Systems (CHI EA '12). ACM, New York, NY, USA, 885-910.&lt;br/&gt;<br /> [27] Amit Sheth. &quot;Physical Cyber Social Computing for Human Experience.&quot; International Conference on Web Intelligence, Mining and Semantics (WIMS-13), Madrid, Spain, June 12-14, 2013. Keynote.&lt;br /&gt;<br /> <br /> ==Citation==<br /> Amit Sheth, Pramod Anantharam, Cory Henson, [http://knoesis.org/library/resource.php?id=1816 'Physical-Cyber-Social Computing: An Early 21st Century Approach,'] IEEE Intelligent Systems, pp. 79-82, Jan./Feb. 2013<br /> <br /> <br /> <br /> ==Related Writings, Talks and Events==<br /> * [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations], PCS Application in Sustainability, Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12-17, Phoenix, Arizona, USA<br /> * [http://www.knoesis.org/library/resource.php?id=2153 Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children], PCS Application in Healthcare, IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA<br /> * [http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13402 Dagstuhl Seminar on Physical-Cyber-Social Computing] (Organizers: Barnaghi P., Jain R., Sheth A., Staab S., Strohmier M.), Dagstuhl Seminar, September 30-October 04, 2013.<br /> * A. Sheth, [http://www.slideshare.net/apsheth/physical-cyber-social-computing-an-early-21st-century-approach-to-computing-for-human-experience Physical-Cyber-Social-Computing An early 21st century approach to Computing for Human Experience] Keynote at The International Conference on Web Intelligence, Mining and Semantics (WIMS 13), July 12-14, 2013. [http://videolectures.net/wims2013_sheth_physical_cyber_social_computing/ Video of the KeyNote]<br /> * A. Sheth, P. Anantharam, [http://knoesis.org/library/resource.php?id=1884 Physical Cyber Social Computing for Human Experience], Intl Conf on Web Intelligence, Mining and Semantics (WIMS2013), June 12-14, 2013, Madrid, Spain (Invited Paper).<br /> * Krishnaprasad Thirunarayan and Amit Sheth, &quot; Semantics-empowered Approaches to [http://www.knoesis.org/library/resource.php?id=1903 Big Data Processing] for Physical-Cyber-Social Applications,&quot; Kno.e.sis Technical Report, May 14, 2013.<br /> * A. Sheth, [http://www.slideshare.net/apsheth/physical-cyber-social-computing Physical Cyber Social Computing], Talk at What will the Semantic Web look like 10 years from now? (SW2022 at ISWC2012), November 11, 2012.<br /> * A. Sheth, [http://amitsheth.blogspot.com/2012/08/semantics-empowered-physical-cyber.html Semantics empowered Physical-Cyber-Social systems], Blog, August 12, 2012<br /> <br /> Also see: [http://knoesis.org/index.php/Computing_For_Human_Experience Computing for Human Experience]</div>

Pramod https://wiki.aiisc.ai/index.php?title=PCS&diff=9418 2016-01-04T15:48:00Z

<p>Pramod: /* Related Writings, Talks and Events */</p> <hr /> <div>&lt;span style=&quot;font-size:26pt;color:purple&quot;&gt;Physical-Cyber-Social Computing: An Early 21st Century Approach to &lt;br /&gt;&lt;br /&gt; Computing for Human Experience&lt;/span&gt; &lt;br /&gt;&lt;br /&gt;<br /> <br /> [http://knoesis.org/amit Amit Sheth], [http://knoesis.org/researchers/pramod/ Pramod Anantharam], and [http://knoesis.org/researchers/cory/ Cory Henson]&lt;br /&gt;&lt;br /&gt;<br /> <br /> Visionaries and scientists from the early days of computing and electronic communication have discussed the proper role of technology to improve human experience. Technology now plays an increasingly important role in facilitating and improving personal and social activities and engagements, decision making, interaction with physical and social worlds, generating insights, and just about anything that a human, as an intelligent being, seeks to do. We have used the term Computing for Human Experience (CHE) [1] to capture this essential role of technology in a human centric vision. CHE emphasizes the unobtrusive, supportive, and assistive role of technology in improving human experience, so that technology “takes into account the human world and allows computers themselves to disappear in the background” (Mark Weiser [2]). This can be distinguished from Licklider’s vision of human-computer collaboration [5], Engelbart’s vision of augmenting human intellect [16], and McCarthy’s definition of intelligent machines [11].<br /> <br /> In this article, we present a vision of the future of computing, called physical-cyber-social (PCS) computing. PCS computing is a holistic treatment of data, information, and knowledge from physical, cyber, and social worlds to integrate, understand, correlate, and provide contextually relevant abstractions to humans. PCS computing takes ideas from, but goes significantly beyond, the current progress in cyber-physical systems, socio-technical systems and cyber-social systems to support CHE [3]. We will exemplify future CHE application scenarios in healthcare and traffic management that are supported by (a) a deeper and richer semantic interdependence and interplay between sensors and devices at physical layers, (b) rich technology mediated social interactions, and (c) the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning in order to bridge machine and human perceptions. We will share an example of PCS computing using semantic perception [4], which converts low-level, heterogeneous, multimodal and contextually relevant data into high-level abstractions that can provide insights and assist humans in making complex decisions. The key challenge for PCS computing requires moving away from traditional data processing to multi-tier computation along a data-information-knowledge-wisdom dimension, which supports reasoning to convert data into abstractions that are more familiar, accessible, and understandable by people.<br /> <br /> <br /> <br /> <br /> ==Role of Technology in Human Experience==<br /> Ideas on the ways technology -- including devices, computing and communication -- help humans have taken many forms, including: natural human interfaces and interactions (natural computing [17], gesture computing [18], and intelligence at the interface [19]), ubiquitous computing [2], robotics that have focused on mimicking simple human activities, and current efforts in automating complex human activities such as war. <br /> <br /> These different visions of computing fall within a spectrum between machine-centric computing and human-centric computing. One end of the spectrum that delineates major visions (Figure 1) focuses on making computing more intelligent in order to think and behave like humans, in the vein of Vannever Bush (i.e., Memex [20]). A variety of approaches to creating Artificial Intelligence include recent work in neurocomputing all the way to current discussions of droids with increasingly human-like capabilities. The fundamental premise here is for technology to be as capable as humans, or at least more like humans. This indeed can indirectly serve humans. For CHE, however, we are interested in the other end of the spectrum, where the focus is on developing technology, which directly complements humans and enhances their experiences. Between the two ends of the spectrum occupied by making computing smarter and CHE, the middle ground is occupied by work in advanced Human Computer Interaction (HCI) and augmented human intellect, or Ambient Intelligence. In Ambient Intelligence, the focus has been on making machines surrounding humans behave intelligently, making it more machine-centric. HCI accommodates the human experience of interacting with technology, making it closer to the human-centric vision of CHE.<br /> <br /> [[File:technologies.png|Figure 1: Continuum of the role of technology, from making computers smarter to enhancing natural human experiences.]]<br /> <br /> During the last few decades, and especially in recent years, we have seen accelerated development within each layer of physical, cyber, and social domains that have a strong bearing on human experience. We see a new form of systems developing: the cyber component encompassing computing and communications, and its modern evolution for collective intelligence and physical devices (which have been assisting humans for many years). The role of digital technologies (cyberspace) in impacting the physical [21], intellectual, and social worlds is of particular significance.<br /> <br /> For example, healthcare is suffering from the increasing cost of services and an aging population all over the world. In the U.S., over $30 billion was spent in 2006 for hospital readmissions . Reducing healthcare costs using innovative sensing and analytics to create a sustainable future is a grand challenge. There is a need to provide assisted living for the elderly still in their own homes to minimize costs, enhancing their comfort and overall experience. The full potential of the decreasing cost of sensors that can monitor physiological and physical parameters, low cost networking, and increased use of smart phones is yet to be realized. <br /> <br /> We will consider a healthcare scenario as a motivating example to exemplify the vision of Physical-Cyber-Social (PCS) computing, extending beyond conventional Physical-Cyber systems. For example, consider the case of Ram, an Asian male of age 60 who wants to take care of his health, and yet is aware of the costs. He visits his doctor and receives a blood pressure screening and discovers that his blood pressure is slightly higher than expected: 90 diastolic. Let’s consider two questions that he may have: What is the normal blood pressure of an Asian male of his age? What are the best ways of managing a diastolic blood pressure of 90? To answer these questions, we need access to physiological observations obtained from people of similar characteristics and demographics (Physical). The ethnic, social, cultural, and economic background should be considered for similarity (Social). Moreover, in addition to expert knowledge, the knowledge and experiences of similar people dealing with the same health issue is important (Cyber). Current computing systems such as cyber-physical systems are not capable of answering these questions. We envision that PCS computing will provide answers to such important questions in a holistic manner. <br /> <br /> ==Contemporary Work==<br /> For clarity, we have organized the related work in a progressive path toward PCS computing as shown in Figure 2.<br /> <br /> [[File:pcssystems.png|Figure 2. PCS computing at the heart of physical, cyber, and social worlds]]<br /> <br /> ===Physical-Cyber Systems===<br /> There are a myriad of patient monitoring apps that continuously monitor physical/physiological parameters of a person and report to first responders through the cyber world (e.g. LifeWatch , MedApps , CardioNet , and Intelesense ) . These systems span the physical and cyber worlds. In this case, they monitor physical things, such as physiological parameters of a patient, and deliver information to first responders through cyber infrastructure. However, Ram’s questions cannot be answered by a system that does not understand the social aspects of the question. <br /> <br /> ===Cyber-Social Systems===<br /> Social networks such as Facebook, Twitter, and Myspace fall into the category of cyber-social systems. The actors are in the cyber world, engaging in social activities such as sharing, discussing, and propagating their experiences, opinions, and perceptions. Some of the communities may be specialized, such as healthcare (e.g. PatientsLikeMe, 23andMe) , product reviews (e.g. Amazon, Epinions), and disaster management (e.g. Ushahidi ). Ram has to sift through tens of thousands of documents or discussions to find experiences of similar people. However, the physical component is not addressed and he knows that the answers he finds may not be relevant to him.<br /> <br /> ===Physical-Cyber-Social Systems===<br /> PCS systems involve physical, cyber, and social components, but within current manifestations they are often loosely connected. Quantified Self is an example of a physical-cyber-social system. It involves a community of enthusiasts who monitor health related parameters, such as food intake, exercise, sleep, and other physiological parameters. They analyze these observations to come up with insights that are valuable for their own health, fitness, and overall well-being. These insights are shared through articles, videos, and social events organized in many cities around the world. Ram has collected observations of his blood pressure, which are stored in a database on his computer. He visualizes them and presents them to the Quantified Self community. But, his questions still cannot be answered due to the lack of knowledge of the experiences of other people with a similar condition. <br /> <br /> Observations from these systems are too often stove-piped due to many challenges, such as the semantic integration and sharing of heterogeneous sensor observations, and multiple service providers. Current integration and interaction between physical, cyber, and social worlds is brittle involving limited syntactic interoperability or integration rather than semantic integration. These two challenges have led to significant human involvement in making sense of observations from contextually relevant information from physical, cyber, and social worlds.<br /> <br /> ==Physical-Cyber-Social (PCS) Computing==<br /> The vision of PCS computing extends Licklider’s vision of computing in 1960 [5] by adding social, collective, and personalized computing components.<br /> <br /> Figure 3 depicts PCS computing involving observations, experiences, background knowledge, and perceptions in a goal toward a human centric vision. The observations from the physical world are used to perceive an environment. Our perceptions are strongly influenced by our background knowledge and current observations. By analyzing the observations in the context of our background knowledge, we orient ourselves toward subsequent actions. The decision regarding which action to take is based on our evidence and experiences. The final action is executed and the process repeats in a loop. In Figure 3, the influence of background knowledge on the way we interpret current observations though perceptual inference is shown. Perceptions determine our experience and evolve our background knowledge. Experiences from the social world influence our background knowledge and indirectly shape our perceptions and subsequent experiences. All these interactions are heavily dependent on humans to analyze the connections between physical, cyber, and social worlds. The vision of PCS computing is to consider observations, knowledge, and experiences across PCS layers to provide a more holistic computational framework. <br /> <br /> Physical-Cyber systems can no longer be limited to special purpose embedded systems designed for a single task [22]. Our vision goes beyond the interactions and integration of observations from physical, cyber, and social domains. We envision a deeper semantic integration and interplay between the three layers, as shown in Figure 2. The outer circle in the figure represents systems with shallow integration between physical, cyber, and social systems. PCS computing in the inner circle provides deeper integration, interpretation, and personalization of the physical, cyber, and social worlds.<br /> <br /> [[File:pcscycle.png|Figure 3. Computational Cycle in Physical Cyber Social Computing exemplified with a healthcare example]]<br /> <br /> ==Relationship between PCS and CHE==<br /> John Boyd’s concept of observe, orient, decide, and act (OODA-Loop) [23] provides a useful context to describe PCS computing, involving observations from physical, cyber, and social worlds, and their intelligent processing leading to CHE. In order to show how PCS computing can be used to improve human experience, we first need to be able to speak intelligibly about experience. Experience is a broad term, so we will begin by dividing experience into its component parts. This ontology of human experience is composed of observation (and perception), orientation, decision, and action. These experiential activities are derived from the OODA loop, which will serve as a foundational ontology of human experience. <br /> <br /> * Observe – Observation is the act of examining the environment and gathering data. The environment could be physical, examined through the human senses or machine sensors. The environment could be social, examined through social network technologies such as facebook or twitter. The environment could be cyber, examined through Web technologies such as Wikipedia, Google search, or Wolfram Alpha. <br /> * Orient – Orientation is the act of analyzing the data from the observed environment to form a conceptualization of the situation. That is, integrate and interpret the data, translating it from low-level data into high-level knowledge. Orientation is how we conceptualize and understand a situation, based on “culture, experience, new information, analysis, synthesis, and heritage.”<br /> * Decide – Decision is the act of deliberating and choosing between multiple actions in order to proceed towards a goal. <br /> * Act – Action is the process of engaging in a decided activity. This could involve physical action, such as movement, or gathering additional information through observation, by examining the physical, social, or cyber environment.<br /> We characterize the PCS computing process as an OODA-Loop process and Figure 3 can be viewed as an OODA-loop.<br /> <br /> ==How to resolve the Big Data problem?==<br /> The use of physical, cyber, and social data indeed manifests into a Big Data challenge and opportunity. If the existence of social data leads to a Big Data problem, as with many contemporary social networking systems, with the introduction of physical data, the Big Data problem will explode. The Big Data problem can be divided into the task of managing data with four distinguished characteristics: volume, velocity, variety, and veracity. In our PCS computing approach, semantic computing plays a pivotal role in addressing these challenges. The following sketch describes how semantics-empowered PCS computing will address these challenges:<br /> * The challenge of variety, or heterogeneity of data, is addressed by complementing traditional statistical and information retrieval, lexical, natural language processing techniques with semantic interoperability and integration. The former addresses syntactic and structural/representational interoperability. The latter involves the use of ontologies for semantic descriptions of concepts that data and observations capture, semantic annotation of data and semantic integration or fusion of the data. Semantic services enhanced techniques are also used to model and interact with devices and sensors in the physical world, or agents interfacing with humans.<br /> * The challenge of volume and velocity is addressed by integration and interpretation of data at the source (or as close as possible), through &quot;intelligence at the edge.&quot; This is accomplished by downscaling semantic processing of data and making each resource-constrained device more intelligent, i.e., capable of semantic filtering, integration, and interpretation of streaming heterogeneous data [9].<br /> * The challenge of veracity is addressed by assessing trustworthiness and provenance of observations in the PCS systems. Trustworthiness may have different interpretations in physical, cyber, and social domains. Semantics can play an important role of meaningfully integrating these different notions of trust spanning physical, cyber, and social worlds [24].<br /> <br /> ==PCS Operators==<br /> Besides the semantics-empowered capabilities such as those described above, we expect that the core of PCS computing will be made possible by a series of PCS operators. We describe two such operators as early examples, shown in Figure 4. <br /> <br /> [[File:pcsoperators.png|Figure 4. The DIKW [15] triangle along with two types of operators of PCS computing acting on healthcare related data as an example]]<br /> <br /> ==Horizontal Operators==<br /> Semantic integration of heterogeneous, multi-modal observations play an important role in deeper integration and understanding of observations spanning physical, cyber, and social domains. These operators map multimodal PCS data into concepts to support semantic integration within each level of the multi-tier computation along data-information-knowledge-wisdom dimension. Heterogeneous observations in the domain of healthcare include: (1) machine sensor observations: physical measurements from multiple, heterogeneous, active (active human involvement e.g. blood pressure) and passive (passive human involvement e.g. heart rate) sensors, (2) self-observations (in the form of subjective thoughts, feelings, moods, etc.), (3) social observations (from a network of family, friends, colleagues, etc.), and (4) demographic observations (aggregated characteristics of the population with similar attributes and/or lineage). PCS horizontal operators integrate all these observations (within each layer in Figure 4) using semantics-empowered integration techniques. <br /> <br /> ==Vertical Operators==<br /> Vertical operators are used to transcend observations from a lower level to a higher level in Figure 4. These operators are unique compared to existing operations of ascending through the DIKW triangle in the following two ways: (1) the operator is agnostic of the source of the observations, and (2) the knowledge base is not limited to a formal ontology – it spans from statistical knowledge to social experiences. Semantic perception [4,8,9] is one way of ascending through the triangle.<br /> <br /> ===Semantic Perception===<br /> Perception is the act of translating low-level data, acquired through observation, into high-level knowledge. People have evolved sophisticated mechanisms to perceive their environment, which allow for high-level conceptualizations (or abstractions) of a situation. These abstractions are then used for subsequent decision-making and action, without mentally overwhelming a person through the sheer volume and velocity of incoming sensory signals. Through study in cognitive psychology, scientists now understand that the key to perception is prior knowledge [6][7]. In order to design machines capable of perception, Semantic Web technologies may be utilized to integrate observation data with prior knowledge on the Web (such as Linked Open Data) [4][8][9], in order to interpret and make sense of the data. Traditionally, perception is thought of as the act of abstracting physical sensory signals (i.e., physical data). However, we must expand this notion to envision machine perception capable of utilizing prior knowledge found throughout the Web (cyber) in order to integrate and interpret observations from physical, cyber, and social dimensions. This type of perception is far more encompassing and far-reaching than the capability of any single person.<br /> <br /> “In the next century, planet earth will don an electronic skin. It will use the Internet as a scaffold to support and transmit its sensations. This skin is already being stitched together [10].” With the advance of technologies such as semantic perception, along with PCS computing, this dream is quickly becoming reality. Another concept called the Global Brain [22], envisioning computing to be more encompassing and holistic states that “The best symbiosis of man and computer is where a program learns from humans but notices things they would not.”<br /> <br /> Next we describe two early examples of next generation applications that can be made possible with PCS computing.<br /> <br /> ==Scenarios==<br /> ===Healthcare===<br /> Personalized health and patient empowerment are important themes for the next generation of healthcare technology. Today we have smartphone applications driven by background knowledge, we have an increasing number of cyber-physical systems, such as medical implants for heart related diseases, and we have social media applications for patients to learn from other patients and experts. Nevertheless, medicine and health are complex, with a wide array of variables, including symptoms, vital signs, personal history, family history, medications taken, genetic makeup, diet, exercise regimens, expectations and quality of life choices, social and economic factors, and so on. All these variables need to be put in a social and personal context. As an example, the NIH has identified that a person with diastolic pressure between 86 and 90 may be considered pre-hypertensive, and above 90 classified as stage I hypertension . For a South Asian male such as Ram, however, preferred and normative ranges may be lower. <br /> <br /> Given the rapid growth of the Quantified Self movement and the use of social media for health, it is appropriate to expect that in the future one would have devices taking vital signs with respect to a health concern, and PCS computing technology constantly evaluating health conditions with respect to relevant knowledge (e.g., knowledge derived from the NIH, and other recommendations) as well as social knowledge. For the latter, consider that a South Asian male is likely to have a good cross section of friends with similar genetic and socio-economic profiles, so his health condition can be compared with those of his friends for a more personalized solution. Knowledge that several friends who are close in age, with similar education and socio-economic situations have cardiovascular problems would empower this person to closely compare a broader variety of variables. This leads to a better understanding of his risk profile that wouldn’t be possible without such social knowledge.<br /> <br /> PCS computing would be able to answer Ram’s questions, introduced in the beginning of the article, and as shown in Figure 3. The numbering indicates the ordering of the processes in the cycle. Ram has diastolic blood pressure between 86 and 90 mmHg. The background knowledge from authoritative sources (e.g. NIH) indicates that he is pre-hypertensive. However, knowledge from similar people with the same ailment indicates that Asian males have lower thresholds than average for being diagnosed with hypertension. This background knowledge influences the perception process in inferring a higher risk. PCS computing can provide deeper insights and corrective actions. It is capable of integrating observations, experiences, and knowledge from people with similar ethnic, social, cultural, and economic background. Considering all these aspects while computing solutions, PCS will provide effective, personalized, and actionable information to Ram, as shown in Figure 3. Being an Asian male, it is recommended to reduce his salt intake and substitute it with spices.<br /> <br /> ===Vehicular Traffic===<br /> With an increased demand for resources, cities are under pressure to reduce and optimize resource consumption in a city. Traffic monitoring is part of a broader scope of sustainability applications (e.g. water, energy, public safety) and involves cyber-physical systems. Observations in the domain of traffic span across multiple modalities spanning machine sensors to citizen sensors [12]. Machine sensors include speed sensors, cameras, noise sensors, etc., while citizen sensors include people reporting observations about traffic (such as police, and commuters). All these sensors are monitoring a physical system, the road network, which collectively forms a physical, cyber, and social system. These sensors are interconnected and their observations are available on the web. There are social (textual) observations about various events in a city some of which may influence traffic. PCS computing envisions a holistic approach to computing by considering observations from all these modalities and further exploiting cyber and collective knowledge.<br /> <br /> Figure 5 shows a slow moving traffic event being detected by sensors monitoring the speed of vehicles (red strip) on I-77 South at Ridgewood Road. Current cyber-physical systems [12, 13] do not exploit collective knowledge of the domain of traffic available from existing knowledge bases on the web (e.g. ConceptNet 5 [14], Open Data from city authorities). The knowledge of relationships between events affecting traffic can be derived from these sources. In this example, ConceptNet 5 defines a causal relation between an accident and slow moving traffic. There is a tweet and a news article reporting an accident on Ridgewood Road where the machine sensors detected slow moving traffic. <br /> <br /> Such an event will have a different effect on a person travelling vs. a decision-maker such as traffic authorities. PCS computing will consider the context of this observation by asking questions: Who needs this information? How does this impact a person travelling? How does this impact a decision-maker? How long will these events last? What available knowledge and social experiences can be used in this analysis?<br /> <br /> [[File:traffic.png|Figure 5. A traffic scenario showing physical, cyber, and social observations collectively processed to make sense of a traffic condition.]]<br /> <br /> Considering different modalities for analysis will help us deal with incompleteness (complementary sensor observations) and uncertainty (redundant sensor observations) which prevail in most of the domains making physical-cyber-social observations an important way of dealing with problems in many domains.<br /> <br /> PCS computing involves personalized and contextual processing of observations for enhancing human experience.<br /> <br /> ==Conclusions==<br /> Technologies have been assisting humans to solve problems in many ways. There are many computing theories that exist for solving problems using crisp and well-developed theories. Humans are inundated with a lot of observations from physical, cyber, and social worlds. Yet, we perform integration and interpretation of these observations in a seamless way. <br /> While the approach of computing has been in modeling problems using well-formed theories, we envision two possible ways of achieving seamlessness as humans: (1) Use multiple theories that are well formulated to solve a problem (e.g. probabilistic, logical, statistical), and (2) A fundamentally different approach to computing. <br /> <br /> PCS computing captures a synergetic interaction between computing and humans while providing holistic computational solutions extending physical, cyber, and social worlds. The future of technology will not be primarily about asking questions and receiving relevant documents for search queries. CHE enabled by PCS computing represents a paradigm shift from search-based technology to solution-based technology where knowledge is generated by continuous observation of human activities within the physical, cyber, and social worlds, and to use this knowledge to improve human experience. That is, the vision of PCS computing is to think of computing which translates into action using knowledge from cyber space, an idea that is synergistic to that of the global brain [22].<br /> ==Related Talks and Presentation==<br /> <br /> &lt;html&gt;&lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/JwjPT32ACh2Lbz&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/apsheth/physical-cyber-social-computing&quot; title=&quot;Physical Cyber Social Computing&quot; target=&quot;_blank&quot;&gt;Physical Cyber Social Computing&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/apsheth&quot; target=&quot;_blank&quot;&gt;Amit Sheth&lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;&lt;/html&gt;<br /> <br /> <br /> &lt;html&gt;&lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/Bm7xgNnhlBCYQq&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/apsheth/physical-cyber-social-computing-an-early-21st-century-approach-to-computing-for-human-experience&quot; title=&quot;Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience&quot; target=&quot;_blank&quot;&gt;Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/apsheth&quot; target=&quot;_blank&quot;&gt;Amit Sheth&lt;/a&gt;&lt;/strong&gt; [27]&lt;/div&gt;&lt;/html&gt;<br /> <br /> ==References==<br /> [1] Amit Sheth, 'Computing for Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web,' IEEE Internet Computing (Sp. Issue on Internet Predictions: V. Cerf and M. Singh, Eds.), vol. 14, no. 1, pp. 88-91, Jan./Feb. 2010, doi:10.1109/MIC.2010.4 &lt;br/&gt;<br /> [2] Weiser, Mark. &quot;The computer for the 21st century.&quot; Scientific American 265, no. 3 (1991): 94-104. &lt;br/&gt;<br /> [3] Amit Sheth, Semantics empowered Physical-Cyber-Social systems, In: What will the Semantic Web look like 10 years from now? In conjunction with the 11th International Semantic Web Conference 2012 (ISWC 2012). Boston, USA. November 11-15, 2012 (Workshop date: 11/11/2012). &lt;br/&gt;<br /> [4] C. Henson, A. Sheth, K. Thirunarayan, Semantic Perception: Converting Sensory Observations to Abstractions &lt;br/&gt;<br /> [5] Licklider, J.C.R., &quot;Man-Computer Symbiosis&quot;, IRE Transactions on Human Factors in Electronics, vol. HFE-1, 4-11, March 1960. &lt;br/&gt;<br /> [6] Neisser, U.: Cognition and Reality. Psychology, 218, San Francisco: W.H. Freeman and Company (1976). &lt;br/&gt;<br /> [7] Gregory, R.L.: Knowledge in perception and illusion. In: Philosophical Transactions of the Royal Society of London, 352(1358), pp.1121-1127 (1997). &lt;br/&gt;<br /> [8] Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, vol. 6(4), pp.345-376, 2011. &lt;br/&gt;<br /> [9] Cory Henson, Krishnaprasad Thirunarayan, and Amit Sheth, 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices,' In: Proceedings of 11th International Semantic Web Conference (ISWC 2012), Boston, Massachusetts, USA, November 11-25, 2012. &lt;br/&gt;<br /> [10] Neil Gross, “The Earth Will Don an Electronic Skin,” BusinessWeek, Aug. 1999; www.businessweek.com/1999/99_35/b3644024.htm. &lt;br/&gt;<br /> [11] J. McCarthy, What Is Artificial Intelligence? &lt;br/&gt;<br /> [12] Miller, Mahalia, and Chetan Gupta. &quot;Mining traffic incidents to forecast impact.&quot; Proceedings of the ACM SIGKDD International Workshop on Urban Computing. ACM, 2012. &lt;br/&gt;<br /> [13] Horvitz, Eric J., et al. &quot;Prediction, expectation, and surprise: Methods, designs, and study of a deployed traffic forecasting service.&quot; arXiv preprint arXiv:1207.1352 (2012). &lt;br/&gt;<br /> [14] Speer, Robert, and Catherine Havasi. &quot;Representing general relational knowledge in ConceptNet 5.&quot; International conference on language resources and evaluation (LREC). 2012. &lt;br/&gt;<br /> [15] Ackoff, R. L., &quot;From Data to Wisdom&quot;, Journal of Applies Systems Analysis, Volume 16, 1989 p 3-9.&lt;br/&gt;<br /> [16] Engelbart, Douglas. &quot;Augmenting human intellect: a conceptual framework (1962).&quot; PACKER, Randall and JORDAN, Ken. Multimedia. From Wagner to Virtual Reality. New York: WW Norton &amp; Company (2001): 64-90.&lt;br/&gt;<br /> [17] De Castro, Leandro Nunes. Fundamentals of natural computing: basic concepts, algorithms, and applications. Chapman &amp; Hall/CRC, 2006.&lt;br/&gt;<br /> [18] Mistry, Pranav, and Pattie Maes. &quot;SixthSense: a wearable gestural interface.&quot; ACM SIGGRAPH ASIA 2009 Sketches. ACM, 2009.&lt;br/&gt;<br /> [19] Puerta, Angel R. &quot;The study of models of intelligent interfaces.&quot; Proceedings of the 1st international conference on Intelligent user interfaces. ACM, 1993.&lt;br/&gt;<br /> [20] Bush, Vannevar. &quot;As we may think.&quot; (July 1945): Atlantic Monthly, 101-108.&lt;br/&gt;<br /> [21] John Markoff (2010-10-09). &quot;Google Cars Drive Themselves, in Traffic&quot;. The New York Times (Retrieved 11-28-2012).&lt;br/&gt;<br /> [22] Global Brain, http://www.slideshare.net/timoreilly/towards-a-global-brain-7968429, TEDxSV talk, May 14, 2011 (Retrieved 11-30-2012).&lt;br/&gt;<br /> <br /> [22] White, Jules, et al. &quot;R&amp;D challenges and solutions for mobile cyber-physical applications and supporting Internet services.&quot; Journal of Internet Services and Applications 1.1 (2010): 45-56.<br /> [23] OODA-Loop, http://en.wikipedia.org/wiki/OODA_loop, (Retrieved 11-30-2012).&lt;br/&gt;<br /> [24] Pramod Anantharam, Cory A. Henson, Krishnaprasad Thirunarayan and, Amit P. Sheth, &quot;Trust Model for Semantic Sensor and Social Networks: A Preliminary Report&quot;, Aerospace and Electronics Conference (NAECON), Proceedings of the IEEE 2010 National , vol., no., pp.1-5, 14-16 July 2010.&lt;br/&gt;<br /> [25] Yan Xu, Maribeth Gandy, Sami Deen, Brian Schrank, Kim Spreen, Michael Gorbsky, Timothy White, Evan Barba, Iulian Radu, Jay Bolter, and Blair MacIntyre. 2008. BragFish: exploring physical and social interaction in co-located handheld augmented reality games. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (ACE '08). ACM, New York, NY, USA, 276-283.&lt;br/&gt;<br /> [26] Blake Sawyer, Francis Quek, Wai Choong Wong, Mehul Motani, Sharon Lynn Chu Yew Yee, and Manuel Perez-Quinones. 2012. Using physical-social interactions to support information re-finding. In CHI '12 Extended Abstracts on Human Factors in Computing Systems (CHI EA '12). ACM, New York, NY, USA, 885-910.&lt;br/&gt;<br /> [27] Amit Sheth. &quot;Physical Cyber Social Computing for Human Experience.&quot; International Conference on Web Intelligence, Mining and Semantics (WIMS-13), Madrid, Spain, June 12-14, 2013. Keynote.&lt;br /&gt;<br /> <br /> ==Citation==<br /> Amit Sheth, Pramod Anantharam, Cory Henson, [http://knoesis.org/library/resource.php?id=1816 'Physical-Cyber-Social Computing: An Early 21st Century Approach,'] IEEE Intelligent Systems, pp. 79-82, Jan./Feb. 2013<br /> <br /> <br /> <br /> ==Related Writings, Talks and Events==<br /> * [http://www.knoesis.org/library/resource.php?id=2153 Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children], PCS Application in Healthcare, IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA<br /> * [http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13402 Dagstuhl Seminar on Physical-Cyber-Social Computing] (Organizers: Barnaghi P., Jain R., Sheth A., Staab S., Strohmier M.), Dagstuhl Seminar, September 30-October 04, 2013.<br /> * A. Sheth, [http://www.slideshare.net/apsheth/physical-cyber-social-computing-an-early-21st-century-approach-to-computing-for-human-experience Physical-Cyber-Social-Computing An early 21st century approach to Computing for Human Experience] Keynote at The International Conference on Web Intelligence, Mining and Semantics (WIMS 13), July 12-14, 2013. [http://videolectures.net/wims2013_sheth_physical_cyber_social_computing/ Video of the KeyNote]<br /> * A. Sheth, P. Anantharam, [http://knoesis.org/library/resource.php?id=1884 Physical Cyber Social Computing for Human Experience], Intl Conf on Web Intelligence, Mining and Semantics (WIMS2013), June 12-14, 2013, Madrid, Spain (Invited Paper).<br /> * Krishnaprasad Thirunarayan and Amit Sheth, &quot; Semantics-empowered Approaches to [http://www.knoesis.org/library/resource.php?id=1903 Big Data Processing] for Physical-Cyber-Social Applications,&quot; Kno.e.sis Technical Report, May 14, 2013.<br /> * A. Sheth, [http://www.slideshare.net/apsheth/physical-cyber-social-computing Physical Cyber Social Computing], Talk at What will the Semantic Web look like 10 years from now? (SW2022 at ISWC2012), November 11, 2012.<br /> * A. Sheth, [http://amitsheth.blogspot.com/2012/08/semantics-empowered-physical-cyber.html Semantics empowered Physical-Cyber-Social systems], Blog, August 12, 2012<br /> <br /> Also see: [http://knoesis.org/index.php/Computing_For_Human_Experience Computing for Human Experience]</div>

Pramod https://wiki.aiisc.ai/index.php?title=PCS&diff=9417 2016-01-04T15:46:58Z

<p>Pramod: /* Related Writings, Talks and Events */</p> <hr /> <div>&lt;span style=&quot;font-size:26pt;color:purple&quot;&gt;Physical-Cyber-Social Computing: An Early 21st Century Approach to &lt;br /&gt;&lt;br /&gt; Computing for Human Experience&lt;/span&gt; &lt;br /&gt;&lt;br /&gt;<br /> <br /> [http://knoesis.org/amit Amit Sheth], [http://knoesis.org/researchers/pramod/ Pramod Anantharam], and [http://knoesis.org/researchers/cory/ Cory Henson]&lt;br /&gt;&lt;br /&gt;<br /> <br /> Visionaries and scientists from the early days of computing and electronic communication have discussed the proper role of technology to improve human experience. Technology now plays an increasingly important role in facilitating and improving personal and social activities and engagements, decision making, interaction with physical and social worlds, generating insights, and just about anything that a human, as an intelligent being, seeks to do. We have used the term Computing for Human Experience (CHE) [1] to capture this essential role of technology in a human centric vision. CHE emphasizes the unobtrusive, supportive, and assistive role of technology in improving human experience, so that technology “takes into account the human world and allows computers themselves to disappear in the background” (Mark Weiser [2]). This can be distinguished from Licklider’s vision of human-computer collaboration [5], Engelbart’s vision of augmenting human intellect [16], and McCarthy’s definition of intelligent machines [11].<br /> <br /> In this article, we present a vision of the future of computing, called physical-cyber-social (PCS) computing. PCS computing is a holistic treatment of data, information, and knowledge from physical, cyber, and social worlds to integrate, understand, correlate, and provide contextually relevant abstractions to humans. PCS computing takes ideas from, but goes significantly beyond, the current progress in cyber-physical systems, socio-technical systems and cyber-social systems to support CHE [3]. We will exemplify future CHE application scenarios in healthcare and traffic management that are supported by (a) a deeper and richer semantic interdependence and interplay between sensors and devices at physical layers, (b) rich technology mediated social interactions, and (c) the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning in order to bridge machine and human perceptions. We will share an example of PCS computing using semantic perception [4], which converts low-level, heterogeneous, multimodal and contextually relevant data into high-level abstractions that can provide insights and assist humans in making complex decisions. The key challenge for PCS computing requires moving away from traditional data processing to multi-tier computation along a data-information-knowledge-wisdom dimension, which supports reasoning to convert data into abstractions that are more familiar, accessible, and understandable by people.<br /> <br /> <br /> <br /> <br /> ==Role of Technology in Human Experience==<br /> Ideas on the ways technology -- including devices, computing and communication -- help humans have taken many forms, including: natural human interfaces and interactions (natural computing [17], gesture computing [18], and intelligence at the interface [19]), ubiquitous computing [2], robotics that have focused on mimicking simple human activities, and current efforts in automating complex human activities such as war. <br /> <br /> These different visions of computing fall within a spectrum between machine-centric computing and human-centric computing. One end of the spectrum that delineates major visions (Figure 1) focuses on making computing more intelligent in order to think and behave like humans, in the vein of Vannever Bush (i.e., Memex [20]). A variety of approaches to creating Artificial Intelligence include recent work in neurocomputing all the way to current discussions of droids with increasingly human-like capabilities. The fundamental premise here is for technology to be as capable as humans, or at least more like humans. This indeed can indirectly serve humans. For CHE, however, we are interested in the other end of the spectrum, where the focus is on developing technology, which directly complements humans and enhances their experiences. Between the two ends of the spectrum occupied by making computing smarter and CHE, the middle ground is occupied by work in advanced Human Computer Interaction (HCI) and augmented human intellect, or Ambient Intelligence. In Ambient Intelligence, the focus has been on making machines surrounding humans behave intelligently, making it more machine-centric. HCI accommodates the human experience of interacting with technology, making it closer to the human-centric vision of CHE.<br /> <br /> [[File:technologies.png|Figure 1: Continuum of the role of technology, from making computers smarter to enhancing natural human experiences.]]<br /> <br /> During the last few decades, and especially in recent years, we have seen accelerated development within each layer of physical, cyber, and social domains that have a strong bearing on human experience. We see a new form of systems developing: the cyber component encompassing computing and communications, and its modern evolution for collective intelligence and physical devices (which have been assisting humans for many years). The role of digital technologies (cyberspace) in impacting the physical [21], intellectual, and social worlds is of particular significance.<br /> <br /> For example, healthcare is suffering from the increasing cost of services and an aging population all over the world. In the U.S., over $30 billion was spent in 2006 for hospital readmissions . Reducing healthcare costs using innovative sensing and analytics to create a sustainable future is a grand challenge. There is a need to provide assisted living for the elderly still in their own homes to minimize costs, enhancing their comfort and overall experience. The full potential of the decreasing cost of sensors that can monitor physiological and physical parameters, low cost networking, and increased use of smart phones is yet to be realized. <br /> <br /> We will consider a healthcare scenario as a motivating example to exemplify the vision of Physical-Cyber-Social (PCS) computing, extending beyond conventional Physical-Cyber systems. For example, consider the case of Ram, an Asian male of age 60 who wants to take care of his health, and yet is aware of the costs. He visits his doctor and receives a blood pressure screening and discovers that his blood pressure is slightly higher than expected: 90 diastolic. Let’s consider two questions that he may have: What is the normal blood pressure of an Asian male of his age? What are the best ways of managing a diastolic blood pressure of 90? To answer these questions, we need access to physiological observations obtained from people of similar characteristics and demographics (Physical). The ethnic, social, cultural, and economic background should be considered for similarity (Social). Moreover, in addition to expert knowledge, the knowledge and experiences of similar people dealing with the same health issue is important (Cyber). Current computing systems such as cyber-physical systems are not capable of answering these questions. We envision that PCS computing will provide answers to such important questions in a holistic manner. <br /> <br /> ==Contemporary Work==<br /> For clarity, we have organized the related work in a progressive path toward PCS computing as shown in Figure 2.<br /> <br /> [[File:pcssystems.png|Figure 2. PCS computing at the heart of physical, cyber, and social worlds]]<br /> <br /> ===Physical-Cyber Systems===<br /> There are a myriad of patient monitoring apps that continuously monitor physical/physiological parameters of a person and report to first responders through the cyber world (e.g. LifeWatch , MedApps , CardioNet , and Intelesense ) . These systems span the physical and cyber worlds. In this case, they monitor physical things, such as physiological parameters of a patient, and deliver information to first responders through cyber infrastructure. However, Ram’s questions cannot be answered by a system that does not understand the social aspects of the question. <br /> <br /> ===Cyber-Social Systems===<br /> Social networks such as Facebook, Twitter, and Myspace fall into the category of cyber-social systems. The actors are in the cyber world, engaging in social activities such as sharing, discussing, and propagating their experiences, opinions, and perceptions. Some of the communities may be specialized, such as healthcare (e.g. PatientsLikeMe, 23andMe) , product reviews (e.g. Amazon, Epinions), and disaster management (e.g. Ushahidi ). Ram has to sift through tens of thousands of documents or discussions to find experiences of similar people. However, the physical component is not addressed and he knows that the answers he finds may not be relevant to him.<br /> <br /> ===Physical-Cyber-Social Systems===<br /> PCS systems involve physical, cyber, and social components, but within current manifestations they are often loosely connected. Quantified Self is an example of a physical-cyber-social system. It involves a community of enthusiasts who monitor health related parameters, such as food intake, exercise, sleep, and other physiological parameters. They analyze these observations to come up with insights that are valuable for their own health, fitness, and overall well-being. These insights are shared through articles, videos, and social events organized in many cities around the world. Ram has collected observations of his blood pressure, which are stored in a database on his computer. He visualizes them and presents them to the Quantified Self community. But, his questions still cannot be answered due to the lack of knowledge of the experiences of other people with a similar condition. <br /> <br /> Observations from these systems are too often stove-piped due to many challenges, such as the semantic integration and sharing of heterogeneous sensor observations, and multiple service providers. Current integration and interaction between physical, cyber, and social worlds is brittle involving limited syntactic interoperability or integration rather than semantic integration. These two challenges have led to significant human involvement in making sense of observations from contextually relevant information from physical, cyber, and social worlds.<br /> <br /> ==Physical-Cyber-Social (PCS) Computing==<br /> The vision of PCS computing extends Licklider’s vision of computing in 1960 [5] by adding social, collective, and personalized computing components.<br /> <br /> Figure 3 depicts PCS computing involving observations, experiences, background knowledge, and perceptions in a goal toward a human centric vision. The observations from the physical world are used to perceive an environment. Our perceptions are strongly influenced by our background knowledge and current observations. By analyzing the observations in the context of our background knowledge, we orient ourselves toward subsequent actions. The decision regarding which action to take is based on our evidence and experiences. The final action is executed and the process repeats in a loop. In Figure 3, the influence of background knowledge on the way we interpret current observations though perceptual inference is shown. Perceptions determine our experience and evolve our background knowledge. Experiences from the social world influence our background knowledge and indirectly shape our perceptions and subsequent experiences. All these interactions are heavily dependent on humans to analyze the connections between physical, cyber, and social worlds. The vision of PCS computing is to consider observations, knowledge, and experiences across PCS layers to provide a more holistic computational framework. <br /> <br /> Physical-Cyber systems can no longer be limited to special purpose embedded systems designed for a single task [22]. Our vision goes beyond the interactions and integration of observations from physical, cyber, and social domains. We envision a deeper semantic integration and interplay between the three layers, as shown in Figure 2. The outer circle in the figure represents systems with shallow integration between physical, cyber, and social systems. PCS computing in the inner circle provides deeper integration, interpretation, and personalization of the physical, cyber, and social worlds.<br /> <br /> [[File:pcscycle.png|Figure 3. Computational Cycle in Physical Cyber Social Computing exemplified with a healthcare example]]<br /> <br /> ==Relationship between PCS and CHE==<br /> John Boyd’s concept of observe, orient, decide, and act (OODA-Loop) [23] provides a useful context to describe PCS computing, involving observations from physical, cyber, and social worlds, and their intelligent processing leading to CHE. In order to show how PCS computing can be used to improve human experience, we first need to be able to speak intelligibly about experience. Experience is a broad term, so we will begin by dividing experience into its component parts. This ontology of human experience is composed of observation (and perception), orientation, decision, and action. These experiential activities are derived from the OODA loop, which will serve as a foundational ontology of human experience. <br /> <br /> * Observe – Observation is the act of examining the environment and gathering data. The environment could be physical, examined through the human senses or machine sensors. The environment could be social, examined through social network technologies such as facebook or twitter. The environment could be cyber, examined through Web technologies such as Wikipedia, Google search, or Wolfram Alpha. <br /> * Orient – Orientation is the act of analyzing the data from the observed environment to form a conceptualization of the situation. That is, integrate and interpret the data, translating it from low-level data into high-level knowledge. Orientation is how we conceptualize and understand a situation, based on “culture, experience, new information, analysis, synthesis, and heritage.”<br /> * Decide – Decision is the act of deliberating and choosing between multiple actions in order to proceed towards a goal. <br /> * Act – Action is the process of engaging in a decided activity. This could involve physical action, such as movement, or gathering additional information through observation, by examining the physical, social, or cyber environment.<br /> We characterize the PCS computing process as an OODA-Loop process and Figure 3 can be viewed as an OODA-loop.<br /> <br /> ==How to resolve the Big Data problem?==<br /> The use of physical, cyber, and social data indeed manifests into a Big Data challenge and opportunity. If the existence of social data leads to a Big Data problem, as with many contemporary social networking systems, with the introduction of physical data, the Big Data problem will explode. The Big Data problem can be divided into the task of managing data with four distinguished characteristics: volume, velocity, variety, and veracity. In our PCS computing approach, semantic computing plays a pivotal role in addressing these challenges. The following sketch describes how semantics-empowered PCS computing will address these challenges:<br /> * The challenge of variety, or heterogeneity of data, is addressed by complementing traditional statistical and information retrieval, lexical, natural language processing techniques with semantic interoperability and integration. The former addresses syntactic and structural/representational interoperability. The latter involves the use of ontologies for semantic descriptions of concepts that data and observations capture, semantic annotation of data and semantic integration or fusion of the data. Semantic services enhanced techniques are also used to model and interact with devices and sensors in the physical world, or agents interfacing with humans.<br /> * The challenge of volume and velocity is addressed by integration and interpretation of data at the source (or as close as possible), through &quot;intelligence at the edge.&quot; This is accomplished by downscaling semantic processing of data and making each resource-constrained device more intelligent, i.e., capable of semantic filtering, integration, and interpretation of streaming heterogeneous data [9].<br /> * The challenge of veracity is addressed by assessing trustworthiness and provenance of observations in the PCS systems. Trustworthiness may have different interpretations in physical, cyber, and social domains. Semantics can play an important role of meaningfully integrating these different notions of trust spanning physical, cyber, and social worlds [24].<br /> <br /> ==PCS Operators==<br /> Besides the semantics-empowered capabilities such as those described above, we expect that the core of PCS computing will be made possible by a series of PCS operators. We describe two such operators as early examples, shown in Figure 4. <br /> <br /> [[File:pcsoperators.png|Figure 4. The DIKW [15] triangle along with two types of operators of PCS computing acting on healthcare related data as an example]]<br /> <br /> ==Horizontal Operators==<br /> Semantic integration of heterogeneous, multi-modal observations play an important role in deeper integration and understanding of observations spanning physical, cyber, and social domains. These operators map multimodal PCS data into concepts to support semantic integration within each level of the multi-tier computation along data-information-knowledge-wisdom dimension. Heterogeneous observations in the domain of healthcare include: (1) machine sensor observations: physical measurements from multiple, heterogeneous, active (active human involvement e.g. blood pressure) and passive (passive human involvement e.g. heart rate) sensors, (2) self-observations (in the form of subjective thoughts, feelings, moods, etc.), (3) social observations (from a network of family, friends, colleagues, etc.), and (4) demographic observations (aggregated characteristics of the population with similar attributes and/or lineage). PCS horizontal operators integrate all these observations (within each layer in Figure 4) using semantics-empowered integration techniques. <br /> <br /> ==Vertical Operators==<br /> Vertical operators are used to transcend observations from a lower level to a higher level in Figure 4. These operators are unique compared to existing operations of ascending through the DIKW triangle in the following two ways: (1) the operator is agnostic of the source of the observations, and (2) the knowledge base is not limited to a formal ontology – it spans from statistical knowledge to social experiences. Semantic perception [4,8,9] is one way of ascending through the triangle.<br /> <br /> ===Semantic Perception===<br /> Perception is the act of translating low-level data, acquired through observation, into high-level knowledge. People have evolved sophisticated mechanisms to perceive their environment, which allow for high-level conceptualizations (or abstractions) of a situation. These abstractions are then used for subsequent decision-making and action, without mentally overwhelming a person through the sheer volume and velocity of incoming sensory signals. Through study in cognitive psychology, scientists now understand that the key to perception is prior knowledge [6][7]. In order to design machines capable of perception, Semantic Web technologies may be utilized to integrate observation data with prior knowledge on the Web (such as Linked Open Data) [4][8][9], in order to interpret and make sense of the data. Traditionally, perception is thought of as the act of abstracting physical sensory signals (i.e., physical data). However, we must expand this notion to envision machine perception capable of utilizing prior knowledge found throughout the Web (cyber) in order to integrate and interpret observations from physical, cyber, and social dimensions. This type of perception is far more encompassing and far-reaching than the capability of any single person.<br /> <br /> “In the next century, planet earth will don an electronic skin. It will use the Internet as a scaffold to support and transmit its sensations. This skin is already being stitched together [10].” With the advance of technologies such as semantic perception, along with PCS computing, this dream is quickly becoming reality. Another concept called the Global Brain [22], envisioning computing to be more encompassing and holistic states that “The best symbiosis of man and computer is where a program learns from humans but notices things they would not.”<br /> <br /> Next we describe two early examples of next generation applications that can be made possible with PCS computing.<br /> <br /> ==Scenarios==<br /> ===Healthcare===<br /> Personalized health and patient empowerment are important themes for the next generation of healthcare technology. Today we have smartphone applications driven by background knowledge, we have an increasing number of cyber-physical systems, such as medical implants for heart related diseases, and we have social media applications for patients to learn from other patients and experts. Nevertheless, medicine and health are complex, with a wide array of variables, including symptoms, vital signs, personal history, family history, medications taken, genetic makeup, diet, exercise regimens, expectations and quality of life choices, social and economic factors, and so on. All these variables need to be put in a social and personal context. As an example, the NIH has identified that a person with diastolic pressure between 86 and 90 may be considered pre-hypertensive, and above 90 classified as stage I hypertension . For a South Asian male such as Ram, however, preferred and normative ranges may be lower. <br /> <br /> Given the rapid growth of the Quantified Self movement and the use of social media for health, it is appropriate to expect that in the future one would have devices taking vital signs with respect to a health concern, and PCS computing technology constantly evaluating health conditions with respect to relevant knowledge (e.g., knowledge derived from the NIH, and other recommendations) as well as social knowledge. For the latter, consider that a South Asian male is likely to have a good cross section of friends with similar genetic and socio-economic profiles, so his health condition can be compared with those of his friends for a more personalized solution. Knowledge that several friends who are close in age, with similar education and socio-economic situations have cardiovascular problems would empower this person to closely compare a broader variety of variables. This leads to a better understanding of his risk profile that wouldn’t be possible without such social knowledge.<br /> <br /> PCS computing would be able to answer Ram’s questions, introduced in the beginning of the article, and as shown in Figure 3. The numbering indicates the ordering of the processes in the cycle. Ram has diastolic blood pressure between 86 and 90 mmHg. The background knowledge from authoritative sources (e.g. NIH) indicates that he is pre-hypertensive. However, knowledge from similar people with the same ailment indicates that Asian males have lower thresholds than average for being diagnosed with hypertension. This background knowledge influences the perception process in inferring a higher risk. PCS computing can provide deeper insights and corrective actions. It is capable of integrating observations, experiences, and knowledge from people with similar ethnic, social, cultural, and economic background. Considering all these aspects while computing solutions, PCS will provide effective, personalized, and actionable information to Ram, as shown in Figure 3. Being an Asian male, it is recommended to reduce his salt intake and substitute it with spices.<br /> <br /> ===Vehicular Traffic===<br /> With an increased demand for resources, cities are under pressure to reduce and optimize resource consumption in a city. Traffic monitoring is part of a broader scope of sustainability applications (e.g. water, energy, public safety) and involves cyber-physical systems. Observations in the domain of traffic span across multiple modalities spanning machine sensors to citizen sensors [12]. Machine sensors include speed sensors, cameras, noise sensors, etc., while citizen sensors include people reporting observations about traffic (such as police, and commuters). All these sensors are monitoring a physical system, the road network, which collectively forms a physical, cyber, and social system. These sensors are interconnected and their observations are available on the web. There are social (textual) observations about various events in a city some of which may influence traffic. PCS computing envisions a holistic approach to computing by considering observations from all these modalities and further exploiting cyber and collective knowledge.<br /> <br /> Figure 5 shows a slow moving traffic event being detected by sensors monitoring the speed of vehicles (red strip) on I-77 South at Ridgewood Road. Current cyber-physical systems [12, 13] do not exploit collective knowledge of the domain of traffic available from existing knowledge bases on the web (e.g. ConceptNet 5 [14], Open Data from city authorities). The knowledge of relationships between events affecting traffic can be derived from these sources. In this example, ConceptNet 5 defines a causal relation between an accident and slow moving traffic. There is a tweet and a news article reporting an accident on Ridgewood Road where the machine sensors detected slow moving traffic. <br /> <br /> Such an event will have a different effect on a person travelling vs. a decision-maker such as traffic authorities. PCS computing will consider the context of this observation by asking questions: Who needs this information? How does this impact a person travelling? How does this impact a decision-maker? How long will these events last? What available knowledge and social experiences can be used in this analysis?<br /> <br /> [[File:traffic.png|Figure 5. A traffic scenario showing physical, cyber, and social observations collectively processed to make sense of a traffic condition.]]<br /> <br /> Considering different modalities for analysis will help us deal with incompleteness (complementary sensor observations) and uncertainty (redundant sensor observations) which prevail in most of the domains making physical-cyber-social observations an important way of dealing with problems in many domains.<br /> <br /> PCS computing involves personalized and contextual processing of observations for enhancing human experience.<br /> <br /> ==Conclusions==<br /> Technologies have been assisting humans to solve problems in many ways. There are many computing theories that exist for solving problems using crisp and well-developed theories. Humans are inundated with a lot of observations from physical, cyber, and social worlds. Yet, we perform integration and interpretation of these observations in a seamless way. <br /> While the approach of computing has been in modeling problems using well-formed theories, we envision two possible ways of achieving seamlessness as humans: (1) Use multiple theories that are well formulated to solve a problem (e.g. probabilistic, logical, statistical), and (2) A fundamentally different approach to computing. <br /> <br /> PCS computing captures a synergetic interaction between computing and humans while providing holistic computational solutions extending physical, cyber, and social worlds. The future of technology will not be primarily about asking questions and receiving relevant documents for search queries. CHE enabled by PCS computing represents a paradigm shift from search-based technology to solution-based technology where knowledge is generated by continuous observation of human activities within the physical, cyber, and social worlds, and to use this knowledge to improve human experience. That is, the vision of PCS computing is to think of computing which translates into action using knowledge from cyber space, an idea that is synergistic to that of the global brain [22].<br /> ==Related Talks and Presentation==<br /> <br /> &lt;html&gt;&lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/JwjPT32ACh2Lbz&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/apsheth/physical-cyber-social-computing&quot; title=&quot;Physical Cyber Social Computing&quot; target=&quot;_blank&quot;&gt;Physical Cyber Social Computing&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/apsheth&quot; target=&quot;_blank&quot;&gt;Amit Sheth&lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;&lt;/html&gt;<br /> <br /> <br /> &lt;html&gt;&lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/Bm7xgNnhlBCYQq&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/apsheth/physical-cyber-social-computing-an-early-21st-century-approach-to-computing-for-human-experience&quot; title=&quot;Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience&quot; target=&quot;_blank&quot;&gt;Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/apsheth&quot; target=&quot;_blank&quot;&gt;Amit Sheth&lt;/a&gt;&lt;/strong&gt; [27]&lt;/div&gt;&lt;/html&gt;<br /> <br /> ==References==<br /> [1] Amit Sheth, 'Computing for Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web,' IEEE Internet Computing (Sp. Issue on Internet Predictions: V. Cerf and M. Singh, Eds.), vol. 14, no. 1, pp. 88-91, Jan./Feb. 2010, doi:10.1109/MIC.2010.4 &lt;br/&gt;<br /> [2] Weiser, Mark. &quot;The computer for the 21st century.&quot; Scientific American 265, no. 3 (1991): 94-104. &lt;br/&gt;<br /> [3] Amit Sheth, Semantics empowered Physical-Cyber-Social systems, In: What will the Semantic Web look like 10 years from now? In conjunction with the 11th International Semantic Web Conference 2012 (ISWC 2012). Boston, USA. November 11-15, 2012 (Workshop date: 11/11/2012). &lt;br/&gt;<br /> [4] C. Henson, A. Sheth, K. Thirunarayan, Semantic Perception: Converting Sensory Observations to Abstractions &lt;br/&gt;<br /> [5] Licklider, J.C.R., &quot;Man-Computer Symbiosis&quot;, IRE Transactions on Human Factors in Electronics, vol. HFE-1, 4-11, March 1960. &lt;br/&gt;<br /> [6] Neisser, U.: Cognition and Reality. Psychology, 218, San Francisco: W.H. Freeman and Company (1976). &lt;br/&gt;<br /> [7] Gregory, R.L.: Knowledge in perception and illusion. In: Philosophical Transactions of the Royal Society of London, 352(1358), pp.1121-1127 (1997). &lt;br/&gt;<br /> [8] Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, vol. 6(4), pp.345-376, 2011. &lt;br/&gt;<br /> [9] Cory Henson, Krishnaprasad Thirunarayan, and Amit Sheth, 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices,' In: Proceedings of 11th International Semantic Web Conference (ISWC 2012), Boston, Massachusetts, USA, November 11-25, 2012. &lt;br/&gt;<br /> [10] Neil Gross, “The Earth Will Don an Electronic Skin,” BusinessWeek, Aug. 1999; www.businessweek.com/1999/99_35/b3644024.htm. &lt;br/&gt;<br /> [11] J. McCarthy, What Is Artificial Intelligence? &lt;br/&gt;<br /> [12] Miller, Mahalia, and Chetan Gupta. &quot;Mining traffic incidents to forecast impact.&quot; Proceedings of the ACM SIGKDD International Workshop on Urban Computing. ACM, 2012. &lt;br/&gt;<br /> [13] Horvitz, Eric J., et al. &quot;Prediction, expectation, and surprise: Methods, designs, and study of a deployed traffic forecasting service.&quot; arXiv preprint arXiv:1207.1352 (2012). &lt;br/&gt;<br /> [14] Speer, Robert, and Catherine Havasi. &quot;Representing general relational knowledge in ConceptNet 5.&quot; International conference on language resources and evaluation (LREC). 2012. &lt;br/&gt;<br /> [15] Ackoff, R. L., &quot;From Data to Wisdom&quot;, Journal of Applies Systems Analysis, Volume 16, 1989 p 3-9.&lt;br/&gt;<br /> [16] Engelbart, Douglas. &quot;Augmenting human intellect: a conceptual framework (1962).&quot; PACKER, Randall and JORDAN, Ken. Multimedia. From Wagner to Virtual Reality. New York: WW Norton &amp; Company (2001): 64-90.&lt;br/&gt;<br /> [17] De Castro, Leandro Nunes. Fundamentals of natural computing: basic concepts, algorithms, and applications. Chapman &amp; Hall/CRC, 2006.&lt;br/&gt;<br /> [18] Mistry, Pranav, and Pattie Maes. &quot;SixthSense: a wearable gestural interface.&quot; ACM SIGGRAPH ASIA 2009 Sketches. ACM, 2009.&lt;br/&gt;<br /> [19] Puerta, Angel R. &quot;The study of models of intelligent interfaces.&quot; Proceedings of the 1st international conference on Intelligent user interfaces. ACM, 1993.&lt;br/&gt;<br /> [20] Bush, Vannevar. &quot;As we may think.&quot; (July 1945): Atlantic Monthly, 101-108.&lt;br/&gt;<br /> [21] John Markoff (2010-10-09). &quot;Google Cars Drive Themselves, in Traffic&quot;. The New York Times (Retrieved 11-28-2012).&lt;br/&gt;<br /> [22] Global Brain, http://www.slideshare.net/timoreilly/towards-a-global-brain-7968429, TEDxSV talk, May 14, 2011 (Retrieved 11-30-2012).&lt;br/&gt;<br /> <br /> [22] White, Jules, et al. &quot;R&amp;D challenges and solutions for mobile cyber-physical applications and supporting Internet services.&quot; Journal of Internet Services and Applications 1.1 (2010): 45-56.<br /> [23] OODA-Loop, http://en.wikipedia.org/wiki/OODA_loop, (Retrieved 11-30-2012).&lt;br/&gt;<br /> [24] Pramod Anantharam, Cory A. Henson, Krishnaprasad Thirunarayan and, Amit P. Sheth, &quot;Trust Model for Semantic Sensor and Social Networks: A Preliminary Report&quot;, Aerospace and Electronics Conference (NAECON), Proceedings of the IEEE 2010 National , vol., no., pp.1-5, 14-16 July 2010.&lt;br/&gt;<br /> [25] Yan Xu, Maribeth Gandy, Sami Deen, Brian Schrank, Kim Spreen, Michael Gorbsky, Timothy White, Evan Barba, Iulian Radu, Jay Bolter, and Blair MacIntyre. 2008. BragFish: exploring physical and social interaction in co-located handheld augmented reality games. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (ACE '08). ACM, New York, NY, USA, 276-283.&lt;br/&gt;<br /> [26] Blake Sawyer, Francis Quek, Wai Choong Wong, Mehul Motani, Sharon Lynn Chu Yew Yee, and Manuel Perez-Quinones. 2012. Using physical-social interactions to support information re-finding. In CHI '12 Extended Abstracts on Human Factors in Computing Systems (CHI EA '12). ACM, New York, NY, USA, 885-910.&lt;br/&gt;<br /> [27] Amit Sheth. &quot;Physical Cyber Social Computing for Human Experience.&quot; International Conference on Web Intelligence, Mining and Semantics (WIMS-13), Madrid, Spain, June 12-14, 2013. Keynote.&lt;br /&gt;<br /> <br /> ==Citation==<br /> Amit Sheth, Pramod Anantharam, Cory Henson, [http://knoesis.org/library/resource.php?id=1816 'Physical-Cyber-Social Computing: An Early 21st Century Approach,'] IEEE Intelligent Systems, pp. 79-82, Jan./Feb. 2013<br /> <br /> <br /> <br /> ==Related Writings, Talks and Events==<br /> * [http://www.knoesis.org/library/resource.php?id=2153 Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children], PCS application in healthcare, IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA<br /> * [http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13402 Dagstuhl Seminar on Physical-Cyber-Social Computing] (Organizers: Barnaghi P., Jain R., Sheth A., Staab S., Strohmier M.), Dagstuhl Seminar, September 30-October 04, 2013.<br /> * A. Sheth, [http://www.slideshare.net/apsheth/physical-cyber-social-computing-an-early-21st-century-approach-to-computing-for-human-experience Physical-Cyber-Social-Computing An early 21st century approach to Computing for Human Experience] Keynote at The International Conference on Web Intelligence, Mining and Semantics (WIMS 13), July 12-14, 2013. [http://videolectures.net/wims2013_sheth_physical_cyber_social_computing/ Video of the KeyNote]<br /> * A. Sheth, P. Anantharam, [http://knoesis.org/library/resource.php?id=1884 Physical Cyber Social Computing for Human Experience], Intl Conf on Web Intelligence, Mining and Semantics (WIMS2013), June 12-14, 2013, Madrid, Spain (Invited Paper).<br /> * Krishnaprasad Thirunarayan and Amit Sheth, &quot; Semantics-empowered Approaches to [http://www.knoesis.org/library/resource.php?id=1903 Big Data Processing] for Physical-Cyber-Social Applications,&quot; Kno.e.sis Technical Report, May 14, 2013.<br /> * A. Sheth, [http://www.slideshare.net/apsheth/physical-cyber-social-computing Physical Cyber Social Computing], Talk at What will the Semantic Web look like 10 years from now? (SW2022 at ISWC2012), November 11, 2012.<br /> * A. Sheth, [http://amitsheth.blogspot.com/2012/08/semantics-empowered-physical-cyber.html Semantics empowered Physical-Cyber-Social systems], Blog, August 12, 2012<br /> <br /> Also see: [http://knoesis.org/index.php/Computing_For_Human_Experience Computing for Human Experience]</div>

Pramod https://wiki.aiisc.ai/index.php?title=PCS&diff=9416 2016-01-04T15:44:22Z

<p>Pramod: /* Related Writings, Talks and Events */</p> <hr /> <div>&lt;span style=&quot;font-size:26pt;color:purple&quot;&gt;Physical-Cyber-Social Computing: An Early 21st Century Approach to &lt;br /&gt;&lt;br /&gt; Computing for Human Experience&lt;/span&gt; &lt;br /&gt;&lt;br /&gt;<br /> <br /> [http://knoesis.org/amit Amit Sheth], [http://knoesis.org/researchers/pramod/ Pramod Anantharam], and [http://knoesis.org/researchers/cory/ Cory Henson]&lt;br /&gt;&lt;br /&gt;<br /> <br /> Visionaries and scientists from the early days of computing and electronic communication have discussed the proper role of technology to improve human experience. Technology now plays an increasingly important role in facilitating and improving personal and social activities and engagements, decision making, interaction with physical and social worlds, generating insights, and just about anything that a human, as an intelligent being, seeks to do. We have used the term Computing for Human Experience (CHE) [1] to capture this essential role of technology in a human centric vision. CHE emphasizes the unobtrusive, supportive, and assistive role of technology in improving human experience, so that technology “takes into account the human world and allows computers themselves to disappear in the background” (Mark Weiser [2]). This can be distinguished from Licklider’s vision of human-computer collaboration [5], Engelbart’s vision of augmenting human intellect [16], and McCarthy’s definition of intelligent machines [11].<br /> <br /> In this article, we present a vision of the future of computing, called physical-cyber-social (PCS) computing. PCS computing is a holistic treatment of data, information, and knowledge from physical, cyber, and social worlds to integrate, understand, correlate, and provide contextually relevant abstractions to humans. PCS computing takes ideas from, but goes significantly beyond, the current progress in cyber-physical systems, socio-technical systems and cyber-social systems to support CHE [3]. We will exemplify future CHE application scenarios in healthcare and traffic management that are supported by (a) a deeper and richer semantic interdependence and interplay between sensors and devices at physical layers, (b) rich technology mediated social interactions, and (c) the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning in order to bridge machine and human perceptions. We will share an example of PCS computing using semantic perception [4], which converts low-level, heterogeneous, multimodal and contextually relevant data into high-level abstractions that can provide insights and assist humans in making complex decisions. The key challenge for PCS computing requires moving away from traditional data processing to multi-tier computation along a data-information-knowledge-wisdom dimension, which supports reasoning to convert data into abstractions that are more familiar, accessible, and understandable by people.<br /> <br /> <br /> <br /> <br /> ==Role of Technology in Human Experience==<br /> Ideas on the ways technology -- including devices, computing and communication -- help humans have taken many forms, including: natural human interfaces and interactions (natural computing [17], gesture computing [18], and intelligence at the interface [19]), ubiquitous computing [2], robotics that have focused on mimicking simple human activities, and current efforts in automating complex human activities such as war. <br /> <br /> These different visions of computing fall within a spectrum between machine-centric computing and human-centric computing. One end of the spectrum that delineates major visions (Figure 1) focuses on making computing more intelligent in order to think and behave like humans, in the vein of Vannever Bush (i.e., Memex [20]). A variety of approaches to creating Artificial Intelligence include recent work in neurocomputing all the way to current discussions of droids with increasingly human-like capabilities. The fundamental premise here is for technology to be as capable as humans, or at least more like humans. This indeed can indirectly serve humans. For CHE, however, we are interested in the other end of the spectrum, where the focus is on developing technology, which directly complements humans and enhances their experiences. Between the two ends of the spectrum occupied by making computing smarter and CHE, the middle ground is occupied by work in advanced Human Computer Interaction (HCI) and augmented human intellect, or Ambient Intelligence. In Ambient Intelligence, the focus has been on making machines surrounding humans behave intelligently, making it more machine-centric. HCI accommodates the human experience of interacting with technology, making it closer to the human-centric vision of CHE.<br /> <br /> [[File:technologies.png|Figure 1: Continuum of the role of technology, from making computers smarter to enhancing natural human experiences.]]<br /> <br /> During the last few decades, and especially in recent years, we have seen accelerated development within each layer of physical, cyber, and social domains that have a strong bearing on human experience. We see a new form of systems developing: the cyber component encompassing computing and communications, and its modern evolution for collective intelligence and physical devices (which have been assisting humans for many years). The role of digital technologies (cyberspace) in impacting the physical [21], intellectual, and social worlds is of particular significance.<br /> <br /> For example, healthcare is suffering from the increasing cost of services and an aging population all over the world. In the U.S., over $30 billion was spent in 2006 for hospital readmissions . Reducing healthcare costs using innovative sensing and analytics to create a sustainable future is a grand challenge. There is a need to provide assisted living for the elderly still in their own homes to minimize costs, enhancing their comfort and overall experience. The full potential of the decreasing cost of sensors that can monitor physiological and physical parameters, low cost networking, and increased use of smart phones is yet to be realized. <br /> <br /> We will consider a healthcare scenario as a motivating example to exemplify the vision of Physical-Cyber-Social (PCS) computing, extending beyond conventional Physical-Cyber systems. For example, consider the case of Ram, an Asian male of age 60 who wants to take care of his health, and yet is aware of the costs. He visits his doctor and receives a blood pressure screening and discovers that his blood pressure is slightly higher than expected: 90 diastolic. Let’s consider two questions that he may have: What is the normal blood pressure of an Asian male of his age? What are the best ways of managing a diastolic blood pressure of 90? To answer these questions, we need access to physiological observations obtained from people of similar characteristics and demographics (Physical). The ethnic, social, cultural, and economic background should be considered for similarity (Social). Moreover, in addition to expert knowledge, the knowledge and experiences of similar people dealing with the same health issue is important (Cyber). Current computing systems such as cyber-physical systems are not capable of answering these questions. We envision that PCS computing will provide answers to such important questions in a holistic manner. <br /> <br /> ==Contemporary Work==<br /> For clarity, we have organized the related work in a progressive path toward PCS computing as shown in Figure 2.<br /> <br /> [[File:pcssystems.png|Figure 2. PCS computing at the heart of physical, cyber, and social worlds]]<br /> <br /> ===Physical-Cyber Systems===<br /> There are a myriad of patient monitoring apps that continuously monitor physical/physiological parameters of a person and report to first responders through the cyber world (e.g. LifeWatch , MedApps , CardioNet , and Intelesense ) . These systems span the physical and cyber worlds. In this case, they monitor physical things, such as physiological parameters of a patient, and deliver information to first responders through cyber infrastructure. However, Ram’s questions cannot be answered by a system that does not understand the social aspects of the question. <br /> <br /> ===Cyber-Social Systems===<br /> Social networks such as Facebook, Twitter, and Myspace fall into the category of cyber-social systems. The actors are in the cyber world, engaging in social activities such as sharing, discussing, and propagating their experiences, opinions, and perceptions. Some of the communities may be specialized, such as healthcare (e.g. PatientsLikeMe, 23andMe) , product reviews (e.g. Amazon, Epinions), and disaster management (e.g. Ushahidi ). Ram has to sift through tens of thousands of documents or discussions to find experiences of similar people. However, the physical component is not addressed and he knows that the answers he finds may not be relevant to him.<br /> <br /> ===Physical-Cyber-Social Systems===<br /> PCS systems involve physical, cyber, and social components, but within current manifestations they are often loosely connected. Quantified Self is an example of a physical-cyber-social system. It involves a community of enthusiasts who monitor health related parameters, such as food intake, exercise, sleep, and other physiological parameters. They analyze these observations to come up with insights that are valuable for their own health, fitness, and overall well-being. These insights are shared through articles, videos, and social events organized in many cities around the world. Ram has collected observations of his blood pressure, which are stored in a database on his computer. He visualizes them and presents them to the Quantified Self community. But, his questions still cannot be answered due to the lack of knowledge of the experiences of other people with a similar condition. <br /> <br /> Observations from these systems are too often stove-piped due to many challenges, such as the semantic integration and sharing of heterogeneous sensor observations, and multiple service providers. Current integration and interaction between physical, cyber, and social worlds is brittle involving limited syntactic interoperability or integration rather than semantic integration. These two challenges have led to significant human involvement in making sense of observations from contextually relevant information from physical, cyber, and social worlds.<br /> <br /> ==Physical-Cyber-Social (PCS) Computing==<br /> The vision of PCS computing extends Licklider’s vision of computing in 1960 [5] by adding social, collective, and personalized computing components.<br /> <br /> Figure 3 depicts PCS computing involving observations, experiences, background knowledge, and perceptions in a goal toward a human centric vision. The observations from the physical world are used to perceive an environment. Our perceptions are strongly influenced by our background knowledge and current observations. By analyzing the observations in the context of our background knowledge, we orient ourselves toward subsequent actions. The decision regarding which action to take is based on our evidence and experiences. The final action is executed and the process repeats in a loop. In Figure 3, the influence of background knowledge on the way we interpret current observations though perceptual inference is shown. Perceptions determine our experience and evolve our background knowledge. Experiences from the social world influence our background knowledge and indirectly shape our perceptions and subsequent experiences. All these interactions are heavily dependent on humans to analyze the connections between physical, cyber, and social worlds. The vision of PCS computing is to consider observations, knowledge, and experiences across PCS layers to provide a more holistic computational framework. <br /> <br /> Physical-Cyber systems can no longer be limited to special purpose embedded systems designed for a single task [22]. Our vision goes beyond the interactions and integration of observations from physical, cyber, and social domains. We envision a deeper semantic integration and interplay between the three layers, as shown in Figure 2. The outer circle in the figure represents systems with shallow integration between physical, cyber, and social systems. PCS computing in the inner circle provides deeper integration, interpretation, and personalization of the physical, cyber, and social worlds.<br /> <br /> [[File:pcscycle.png|Figure 3. Computational Cycle in Physical Cyber Social Computing exemplified with a healthcare example]]<br /> <br /> ==Relationship between PCS and CHE==<br /> John Boyd’s concept of observe, orient, decide, and act (OODA-Loop) [23] provides a useful context to describe PCS computing, involving observations from physical, cyber, and social worlds, and their intelligent processing leading to CHE. In order to show how PCS computing can be used to improve human experience, we first need to be able to speak intelligibly about experience. Experience is a broad term, so we will begin by dividing experience into its component parts. This ontology of human experience is composed of observation (and perception), orientation, decision, and action. These experiential activities are derived from the OODA loop, which will serve as a foundational ontology of human experience. <br /> <br /> * Observe – Observation is the act of examining the environment and gathering data. The environment could be physical, examined through the human senses or machine sensors. The environment could be social, examined through social network technologies such as facebook or twitter. The environment could be cyber, examined through Web technologies such as Wikipedia, Google search, or Wolfram Alpha. <br /> * Orient – Orientation is the act of analyzing the data from the observed environment to form a conceptualization of the situation. That is, integrate and interpret the data, translating it from low-level data into high-level knowledge. Orientation is how we conceptualize and understand a situation, based on “culture, experience, new information, analysis, synthesis, and heritage.”<br /> * Decide – Decision is the act of deliberating and choosing between multiple actions in order to proceed towards a goal. <br /> * Act – Action is the process of engaging in a decided activity. This could involve physical action, such as movement, or gathering additional information through observation, by examining the physical, social, or cyber environment.<br /> We characterize the PCS computing process as an OODA-Loop process and Figure 3 can be viewed as an OODA-loop.<br /> <br /> ==How to resolve the Big Data problem?==<br /> The use of physical, cyber, and social data indeed manifests into a Big Data challenge and opportunity. If the existence of social data leads to a Big Data problem, as with many contemporary social networking systems, with the introduction of physical data, the Big Data problem will explode. The Big Data problem can be divided into the task of managing data with four distinguished characteristics: volume, velocity, variety, and veracity. In our PCS computing approach, semantic computing plays a pivotal role in addressing these challenges. The following sketch describes how semantics-empowered PCS computing will address these challenges:<br /> * The challenge of variety, or heterogeneity of data, is addressed by complementing traditional statistical and information retrieval, lexical, natural language processing techniques with semantic interoperability and integration. The former addresses syntactic and structural/representational interoperability. The latter involves the use of ontologies for semantic descriptions of concepts that data and observations capture, semantic annotation of data and semantic integration or fusion of the data. Semantic services enhanced techniques are also used to model and interact with devices and sensors in the physical world, or agents interfacing with humans.<br /> * The challenge of volume and velocity is addressed by integration and interpretation of data at the source (or as close as possible), through &quot;intelligence at the edge.&quot; This is accomplished by downscaling semantic processing of data and making each resource-constrained device more intelligent, i.e., capable of semantic filtering, integration, and interpretation of streaming heterogeneous data [9].<br /> * The challenge of veracity is addressed by assessing trustworthiness and provenance of observations in the PCS systems. Trustworthiness may have different interpretations in physical, cyber, and social domains. Semantics can play an important role of meaningfully integrating these different notions of trust spanning physical, cyber, and social worlds [24].<br /> <br /> ==PCS Operators==<br /> Besides the semantics-empowered capabilities such as those described above, we expect that the core of PCS computing will be made possible by a series of PCS operators. We describe two such operators as early examples, shown in Figure 4. <br /> <br /> [[File:pcsoperators.png|Figure 4. The DIKW [15] triangle along with two types of operators of PCS computing acting on healthcare related data as an example]]<br /> <br /> ==Horizontal Operators==<br /> Semantic integration of heterogeneous, multi-modal observations play an important role in deeper integration and understanding of observations spanning physical, cyber, and social domains. These operators map multimodal PCS data into concepts to support semantic integration within each level of the multi-tier computation along data-information-knowledge-wisdom dimension. Heterogeneous observations in the domain of healthcare include: (1) machine sensor observations: physical measurements from multiple, heterogeneous, active (active human involvement e.g. blood pressure) and passive (passive human involvement e.g. heart rate) sensors, (2) self-observations (in the form of subjective thoughts, feelings, moods, etc.), (3) social observations (from a network of family, friends, colleagues, etc.), and (4) demographic observations (aggregated characteristics of the population with similar attributes and/or lineage). PCS horizontal operators integrate all these observations (within each layer in Figure 4) using semantics-empowered integration techniques. <br /> <br /> ==Vertical Operators==<br /> Vertical operators are used to transcend observations from a lower level to a higher level in Figure 4. These operators are unique compared to existing operations of ascending through the DIKW triangle in the following two ways: (1) the operator is agnostic of the source of the observations, and (2) the knowledge base is not limited to a formal ontology – it spans from statistical knowledge to social experiences. Semantic perception [4,8,9] is one way of ascending through the triangle.<br /> <br /> ===Semantic Perception===<br /> Perception is the act of translating low-level data, acquired through observation, into high-level knowledge. People have evolved sophisticated mechanisms to perceive their environment, which allow for high-level conceptualizations (or abstractions) of a situation. These abstractions are then used for subsequent decision-making and action, without mentally overwhelming a person through the sheer volume and velocity of incoming sensory signals. Through study in cognitive psychology, scientists now understand that the key to perception is prior knowledge [6][7]. In order to design machines capable of perception, Semantic Web technologies may be utilized to integrate observation data with prior knowledge on the Web (such as Linked Open Data) [4][8][9], in order to interpret and make sense of the data. Traditionally, perception is thought of as the act of abstracting physical sensory signals (i.e., physical data). However, we must expand this notion to envision machine perception capable of utilizing prior knowledge found throughout the Web (cyber) in order to integrate and interpret observations from physical, cyber, and social dimensions. This type of perception is far more encompassing and far-reaching than the capability of any single person.<br /> <br /> “In the next century, planet earth will don an electronic skin. It will use the Internet as a scaffold to support and transmit its sensations. This skin is already being stitched together [10].” With the advance of technologies such as semantic perception, along with PCS computing, this dream is quickly becoming reality. Another concept called the Global Brain [22], envisioning computing to be more encompassing and holistic states that “The best symbiosis of man and computer is where a program learns from humans but notices things they would not.”<br /> <br /> Next we describe two early examples of next generation applications that can be made possible with PCS computing.<br /> <br /> ==Scenarios==<br /> ===Healthcare===<br /> Personalized health and patient empowerment are important themes for the next generation of healthcare technology. Today we have smartphone applications driven by background knowledge, we have an increasing number of cyber-physical systems, such as medical implants for heart related diseases, and we have social media applications for patients to learn from other patients and experts. Nevertheless, medicine and health are complex, with a wide array of variables, including symptoms, vital signs, personal history, family history, medications taken, genetic makeup, diet, exercise regimens, expectations and quality of life choices, social and economic factors, and so on. All these variables need to be put in a social and personal context. As an example, the NIH has identified that a person with diastolic pressure between 86 and 90 may be considered pre-hypertensive, and above 90 classified as stage I hypertension . For a South Asian male such as Ram, however, preferred and normative ranges may be lower. <br /> <br /> Given the rapid growth of the Quantified Self movement and the use of social media for health, it is appropriate to expect that in the future one would have devices taking vital signs with respect to a health concern, and PCS computing technology constantly evaluating health conditions with respect to relevant knowledge (e.g., knowledge derived from the NIH, and other recommendations) as well as social knowledge. For the latter, consider that a South Asian male is likely to have a good cross section of friends with similar genetic and socio-economic profiles, so his health condition can be compared with those of his friends for a more personalized solution. Knowledge that several friends who are close in age, with similar education and socio-economic situations have cardiovascular problems would empower this person to closely compare a broader variety of variables. This leads to a better understanding of his risk profile that wouldn’t be possible without such social knowledge.<br /> <br /> PCS computing would be able to answer Ram’s questions, introduced in the beginning of the article, and as shown in Figure 3. The numbering indicates the ordering of the processes in the cycle. Ram has diastolic blood pressure between 86 and 90 mmHg. The background knowledge from authoritative sources (e.g. NIH) indicates that he is pre-hypertensive. However, knowledge from similar people with the same ailment indicates that Asian males have lower thresholds than average for being diagnosed with hypertension. This background knowledge influences the perception process in inferring a higher risk. PCS computing can provide deeper insights and corrective actions. It is capable of integrating observations, experiences, and knowledge from people with similar ethnic, social, cultural, and economic background. Considering all these aspects while computing solutions, PCS will provide effective, personalized, and actionable information to Ram, as shown in Figure 3. Being an Asian male, it is recommended to reduce his salt intake and substitute it with spices.<br /> <br /> ===Vehicular Traffic===<br /> With an increased demand for resources, cities are under pressure to reduce and optimize resource consumption in a city. Traffic monitoring is part of a broader scope of sustainability applications (e.g. water, energy, public safety) and involves cyber-physical systems. Observations in the domain of traffic span across multiple modalities spanning machine sensors to citizen sensors [12]. Machine sensors include speed sensors, cameras, noise sensors, etc., while citizen sensors include people reporting observations about traffic (such as police, and commuters). All these sensors are monitoring a physical system, the road network, which collectively forms a physical, cyber, and social system. These sensors are interconnected and their observations are available on the web. There are social (textual) observations about various events in a city some of which may influence traffic. PCS computing envisions a holistic approach to computing by considering observations from all these modalities and further exploiting cyber and collective knowledge.<br /> <br /> Figure 5 shows a slow moving traffic event being detected by sensors monitoring the speed of vehicles (red strip) on I-77 South at Ridgewood Road. Current cyber-physical systems [12, 13] do not exploit collective knowledge of the domain of traffic available from existing knowledge bases on the web (e.g. ConceptNet 5 [14], Open Data from city authorities). The knowledge of relationships between events affecting traffic can be derived from these sources. In this example, ConceptNet 5 defines a causal relation between an accident and slow moving traffic. There is a tweet and a news article reporting an accident on Ridgewood Road where the machine sensors detected slow moving traffic. <br /> <br /> Such an event will have a different effect on a person travelling vs. a decision-maker such as traffic authorities. PCS computing will consider the context of this observation by asking questions: Who needs this information? How does this impact a person travelling? How does this impact a decision-maker? How long will these events last? What available knowledge and social experiences can be used in this analysis?<br /> <br /> [[File:traffic.png|Figure 5. A traffic scenario showing physical, cyber, and social observations collectively processed to make sense of a traffic condition.]]<br /> <br /> Considering different modalities for analysis will help us deal with incompleteness (complementary sensor observations) and uncertainty (redundant sensor observations) which prevail in most of the domains making physical-cyber-social observations an important way of dealing with problems in many domains.<br /> <br /> PCS computing involves personalized and contextual processing of observations for enhancing human experience.<br /> <br /> ==Conclusions==<br /> Technologies have been assisting humans to solve problems in many ways. There are many computing theories that exist for solving problems using crisp and well-developed theories. Humans are inundated with a lot of observations from physical, cyber, and social worlds. Yet, we perform integration and interpretation of these observations in a seamless way. <br /> While the approach of computing has been in modeling problems using well-formed theories, we envision two possible ways of achieving seamlessness as humans: (1) Use multiple theories that are well formulated to solve a problem (e.g. probabilistic, logical, statistical), and (2) A fundamentally different approach to computing. <br /> <br /> PCS computing captures a synergetic interaction between computing and humans while providing holistic computational solutions extending physical, cyber, and social worlds. The future of technology will not be primarily about asking questions and receiving relevant documents for search queries. CHE enabled by PCS computing represents a paradigm shift from search-based technology to solution-based technology where knowledge is generated by continuous observation of human activities within the physical, cyber, and social worlds, and to use this knowledge to improve human experience. That is, the vision of PCS computing is to think of computing which translates into action using knowledge from cyber space, an idea that is synergistic to that of the global brain [22].<br /> ==Related Talks and Presentation==<br /> <br /> &lt;html&gt;&lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/JwjPT32ACh2Lbz&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/apsheth/physical-cyber-social-computing&quot; title=&quot;Physical Cyber Social Computing&quot; target=&quot;_blank&quot;&gt;Physical Cyber Social Computing&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/apsheth&quot; target=&quot;_blank&quot;&gt;Amit Sheth&lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;&lt;/html&gt;<br /> <br /> <br /> &lt;html&gt;&lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/Bm7xgNnhlBCYQq&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/apsheth/physical-cyber-social-computing-an-early-21st-century-approach-to-computing-for-human-experience&quot; title=&quot;Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience&quot; target=&quot;_blank&quot;&gt;Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/apsheth&quot; target=&quot;_blank&quot;&gt;Amit Sheth&lt;/a&gt;&lt;/strong&gt; [27]&lt;/div&gt;&lt;/html&gt;<br /> <br /> ==References==<br /> [1] Amit Sheth, 'Computing for Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web,' IEEE Internet Computing (Sp. Issue on Internet Predictions: V. Cerf and M. Singh, Eds.), vol. 14, no. 1, pp. 88-91, Jan./Feb. 2010, doi:10.1109/MIC.2010.4 &lt;br/&gt;<br /> [2] Weiser, Mark. &quot;The computer for the 21st century.&quot; Scientific American 265, no. 3 (1991): 94-104. &lt;br/&gt;<br /> [3] Amit Sheth, Semantics empowered Physical-Cyber-Social systems, In: What will the Semantic Web look like 10 years from now? In conjunction with the 11th International Semantic Web Conference 2012 (ISWC 2012). Boston, USA. November 11-15, 2012 (Workshop date: 11/11/2012). &lt;br/&gt;<br /> [4] C. Henson, A. Sheth, K. Thirunarayan, Semantic Perception: Converting Sensory Observations to Abstractions &lt;br/&gt;<br /> [5] Licklider, J.C.R., &quot;Man-Computer Symbiosis&quot;, IRE Transactions on Human Factors in Electronics, vol. HFE-1, 4-11, March 1960. &lt;br/&gt;<br /> [6] Neisser, U.: Cognition and Reality. Psychology, 218, San Francisco: W.H. Freeman and Company (1976). &lt;br/&gt;<br /> [7] Gregory, R.L.: Knowledge in perception and illusion. In: Philosophical Transactions of the Royal Society of London, 352(1358), pp.1121-1127 (1997). &lt;br/&gt;<br /> [8] Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, vol. 6(4), pp.345-376, 2011. &lt;br/&gt;<br /> [9] Cory Henson, Krishnaprasad Thirunarayan, and Amit Sheth, 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices,' In: Proceedings of 11th International Semantic Web Conference (ISWC 2012), Boston, Massachusetts, USA, November 11-25, 2012. &lt;br/&gt;<br /> [10] Neil Gross, “The Earth Will Don an Electronic Skin,” BusinessWeek, Aug. 1999; www.businessweek.com/1999/99_35/b3644024.htm. &lt;br/&gt;<br /> [11] J. McCarthy, What Is Artificial Intelligence? &lt;br/&gt;<br /> [12] Miller, Mahalia, and Chetan Gupta. &quot;Mining traffic incidents to forecast impact.&quot; Proceedings of the ACM SIGKDD International Workshop on Urban Computing. ACM, 2012. &lt;br/&gt;<br /> [13] Horvitz, Eric J., et al. &quot;Prediction, expectation, and surprise: Methods, designs, and study of a deployed traffic forecasting service.&quot; arXiv preprint arXiv:1207.1352 (2012). &lt;br/&gt;<br /> [14] Speer, Robert, and Catherine Havasi. &quot;Representing general relational knowledge in ConceptNet 5.&quot; International conference on language resources and evaluation (LREC). 2012. &lt;br/&gt;<br /> [15] Ackoff, R. L., &quot;From Data to Wisdom&quot;, Journal of Applies Systems Analysis, Volume 16, 1989 p 3-9.&lt;br/&gt;<br /> [16] Engelbart, Douglas. &quot;Augmenting human intellect: a conceptual framework (1962).&quot; PACKER, Randall and JORDAN, Ken. Multimedia. From Wagner to Virtual Reality. New York: WW Norton &amp; Company (2001): 64-90.&lt;br/&gt;<br /> [17] De Castro, Leandro Nunes. Fundamentals of natural computing: basic concepts, algorithms, and applications. Chapman &amp; Hall/CRC, 2006.&lt;br/&gt;<br /> [18] Mistry, Pranav, and Pattie Maes. &quot;SixthSense: a wearable gestural interface.&quot; ACM SIGGRAPH ASIA 2009 Sketches. ACM, 2009.&lt;br/&gt;<br /> [19] Puerta, Angel R. &quot;The study of models of intelligent interfaces.&quot; Proceedings of the 1st international conference on Intelligent user interfaces. ACM, 1993.&lt;br/&gt;<br /> [20] Bush, Vannevar. &quot;As we may think.&quot; (July 1945): Atlantic Monthly, 101-108.&lt;br/&gt;<br /> [21] John Markoff (2010-10-09). &quot;Google Cars Drive Themselves, in Traffic&quot;. The New York Times (Retrieved 11-28-2012).&lt;br/&gt;<br /> [22] Global Brain, http://www.slideshare.net/timoreilly/towards-a-global-brain-7968429, TEDxSV talk, May 14, 2011 (Retrieved 11-30-2012).&lt;br/&gt;<br /> <br /> [22] White, Jules, et al. &quot;R&amp;D challenges and solutions for mobile cyber-physical applications and supporting Internet services.&quot; Journal of Internet Services and Applications 1.1 (2010): 45-56.<br /> [23] OODA-Loop, http://en.wikipedia.org/wiki/OODA_loop, (Retrieved 11-30-2012).&lt;br/&gt;<br /> [24] Pramod Anantharam, Cory A. Henson, Krishnaprasad Thirunarayan and, Amit P. Sheth, &quot;Trust Model for Semantic Sensor and Social Networks: A Preliminary Report&quot;, Aerospace and Electronics Conference (NAECON), Proceedings of the IEEE 2010 National , vol., no., pp.1-5, 14-16 July 2010.&lt;br/&gt;<br /> [25] Yan Xu, Maribeth Gandy, Sami Deen, Brian Schrank, Kim Spreen, Michael Gorbsky, Timothy White, Evan Barba, Iulian Radu, Jay Bolter, and Blair MacIntyre. 2008. BragFish: exploring physical and social interaction in co-located handheld augmented reality games. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (ACE '08). ACM, New York, NY, USA, 276-283.&lt;br/&gt;<br /> [26] Blake Sawyer, Francis Quek, Wai Choong Wong, Mehul Motani, Sharon Lynn Chu Yew Yee, and Manuel Perez-Quinones. 2012. Using physical-social interactions to support information re-finding. In CHI '12 Extended Abstracts on Human Factors in Computing Systems (CHI EA '12). ACM, New York, NY, USA, 885-910.&lt;br/&gt;<br /> [27] Amit Sheth. &quot;Physical Cyber Social Computing for Human Experience.&quot; International Conference on Web Intelligence, Mining and Semantics (WIMS-13), Madrid, Spain, June 12-14, 2013. Keynote.&lt;br /&gt;<br /> <br /> ==Citation==<br /> Amit Sheth, Pramod Anantharam, Cory Henson, [http://knoesis.org/library/resource.php?id=1816 'Physical-Cyber-Social Computing: An Early 21st Century Approach,'] IEEE Intelligent Systems, pp. 79-82, Jan./Feb. 2013<br /> <br /> <br /> <br /> ==Related Writings, Talks and Events==<br /> * [http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13402 Dagstuhl Seminar on Physical-Cyber-Social Computing] (Organizers: Barnaghi P., Jain R., Sheth A., Staab S., Strohmier M.), Dagstuhl Seminar, September 30-October 04, 2013.<br /> * A. Sheth, [http://www.slideshare.net/apsheth/physical-cyber-social-computing-an-early-21st-century-approach-to-computing-for-human-experience Physical-Cyber-Social-Computing An early 21st century approach to Computing for Human Experience] Keynote at The International Conference on Web Intelligence, Mining and Semantics (WIMS 13), July 12-14, 2013. [http://videolectures.net/wims2013_sheth_physical_cyber_social_computing/ Video of the KeyNote]<br /> * A. Sheth, P. Anantharam, [http://knoesis.org/library/resource.php?id=1884 Physical Cyber Social Computing for Human Experience], Intl Conf on Web Intelligence, Mining and Semantics (WIMS2013), June 12-14, 2013, Madrid, Spain (Invited Paper).<br /> * Krishnaprasad Thirunarayan and Amit Sheth, &quot; Semantics-empowered Approaches to [http://www.knoesis.org/library/resource.php?id=1903 Big Data Processing] for Physical-Cyber-Social Applications,&quot; Kno.e.sis Technical Report, May 14, 2013.<br /> * A. Sheth, [http://www.slideshare.net/apsheth/physical-cyber-social-computing Physical Cyber Social Computing], Talk at What will the Semantic Web look like 10 years from now? (SW2022 at ISWC2012), November 11, 2012.<br /> * A. Sheth, [http://amitsheth.blogspot.com/2012/08/semantics-empowered-physical-cyber.html Semantics empowered Physical-Cyber-Social systems], Blog, August 12, 2012<br /> <br /> Also see: [http://knoesis.org/index.php/Computing_For_Human_Experience Computing for Human Experience]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9402 2015-12-10T21:00:00Z

<p>Pramod: /* Broader Research Goals */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> [[File:CityPulseLogo.png|thumb|right]]<br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to a city and their reports of city infrastructure related observations provide unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> [http://www.ict-citypulse.eu/page/ CiytPulse Main Project Page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a two step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Next, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Kno.e.sis/WSU Project Coordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9384 2015-12-09T19:06:02Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> [[File:CityPulseLogo.png|thumb|right]]<br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to a city and their reports of city infrastructure related observations provide unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> [http://www.ict-citypulse.eu/page/ CiytPulse Main Project Page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Kno.e.sis/WSU Project Coordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9383 2015-12-09T19:05:41Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> [[File:CityPulseLogo.png|thumb|right]]<br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to a city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> [http://www.ict-citypulse.eu/page/ CiytPulse Main Project Page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Kno.e.sis/WSU Project Coordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9382 2015-12-09T19:05:15Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> [[File:CityPulseLogo.png|thumb|right]]<br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> [http://www.ict-citypulse.eu/page/ CiytPulse Main Project Page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Kno.e.sis/WSU Project Coordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=File:CityPulseLogo.png&diff=9381 2015-12-09T19:04:45Z

<p>Pramod: </p> <hr /> <div></div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9380 2015-12-09T19:04:27Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> [[File:CityPulseLogo.png|thumb|left]]<br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> [http://www.ict-citypulse.eu/page/ CiytPulse Main Project Page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Kno.e.sis/WSU Project Coordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9379 2015-12-09T19:03:26Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> [[CityPulseLogo.png]]<br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> [http://www.ict-citypulse.eu/page/ CiytPulse Main Project Page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Kno.e.sis/WSU Project Coordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9378 2015-12-09T19:01:56Z

<p>Pramod: /* Project Co-ordinator */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> [http://www.ict-citypulse.eu/page/ CiytPulse Main Project Page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Kno.e.sis/WSU Project Coordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9377 2015-12-09T19:01:08Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> [http://www.ict-citypulse.eu/page/ CiytPulse Main Project Page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9376 2015-12-09T19:00:38Z

<p>Pramod: /* People */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], University of Surrey, UK<br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9375 2015-12-09T19:00:15Z

<p>Pramod: /* People */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Project Coordinator/PI: [http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ Dr. Payam Barnaghi], U of Surrey <br /> *WSU Principal Investigator: [http://knoesis.wright.edu/amit/ Prof. Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Dr. Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9374 2015-12-09T18:56:46Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Principal Investigators: [http://knoesis.wright.edu/amit/ Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9373 2015-12-09T18:56:04Z

<p>Pramod: /* Dynamism */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;425&quot; height=&quot;355&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Principal Investigators: [http://knoesis.wright.edu/amit/ Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9372 2015-12-09T18:55:38Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> &lt;html&gt;<br /> &lt;iframe src=&quot;//www.slideshare.net/slideshow/embed_code/key/epRkybEQJz8N6q&quot; width=&quot;595&quot; height=&quot;485&quot; frameborder=&quot;0&quot; marginwidth=&quot;0&quot; marginheight=&quot;0&quot; scrolling=&quot;no&quot; style=&quot;border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;&quot; allowfullscreen&gt; &lt;/iframe&gt; &lt;div style=&quot;margin-bottom:5px&quot;&gt; &lt;strong&gt; &lt;a href=&quot;//www.slideshare.net/citypulse/city-pulse-overview1&quot; title=&quot;EU FP7 CityPulse Project&quot; target=&quot;_blank&quot;&gt;EU FP7 CityPulse Project&lt;/a&gt; &lt;/strong&gt; from &lt;strong&gt;&lt;a href=&quot;//www.slideshare.net/citypulse&quot; target=&quot;_blank&quot;&gt;CityPulse Project &lt;/a&gt;&lt;/strong&gt; &lt;/div&gt;<br /> &lt;/html&gt;<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Principal Investigators: [http://knoesis.wright.edu/amit/ Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9371 2015-12-09T18:49:14Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Principal Investigators: [http://knoesis.wright.edu/amit/ Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.<br /> <br /> ==Concurrent Projects==<br /> *[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br /> *[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)<br /> *[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br /> *[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br /> *[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] &lt;br /&gt;<br /> *[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br /> *[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9370 2015-12-09T18:48:31Z

<p>Pramod: /* People */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Principal Investigators: [http://knoesis.wright.edu/amit/ Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridaran]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9369 2015-12-09T18:47:47Z

<p>Pramod: /* People */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Principal Investigators: [http://knoesis.wright.edu/amit/ Amit Sheth]<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [https://sites.google.com/a/knoesis.org/tanvi/ Tanvi Banerjee]<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: [http://knoesis.org/researchers/marupudi/ Surendra Marupudi], [http://knoesis.org/researchers/vaikunth/ Vaikunth Sridharan]<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9368 2015-12-09T18:45:21Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> *Principal Investigators: Amit Sheth<br /> *Co-Investigators: CityPulse consortium: University of Surrey (UK), Alexandra Institute (Denmark), Ericsson (Sweden), Siemens (Austria and Romania), National University of Ireland/DERI (Ireland), University of Applied Sciences Osnabrück (Germany), City of Brasov (Romania), City of Aarhus (Denmark)<br /> *Other Collaborators: Krishnaprasad Thirunarayan, Tanvi Banerjee<br /> *PhD Student: [http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> *Master Students: Surendra Marupudi, Vaikunth Sridharan<br /> <br /> ==Project Co-ordinator==<br /> *[http://knoesis.org/researchers/pramod/ Pramod Anantharam]<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9367 2015-12-09T18:42:30Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==People==<br /> Principal Investigators:<br /> Co-Investigators:<br /> Other Collaborators:<br /> PhD Student: Pramod Anantharam<br /> Master Students:<br /> <br /> ==Project Co-ordinator==<br /> Pramod Anantharam<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9366 2015-12-09T17:50:55Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Broader Research Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9365 2015-12-09T17:50:14Z

<p>Pramod: /* Publications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. 2016. [http://www.knoesis.org/library/resource.php?id=2228 Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations.] at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. [http://www.knoesis.org/library/resource.php?id=2030 Extracting City Traffic Events from Social Streams.] ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta. [http://knoesis.org/library/resource.php?id=1954 Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation.] IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, [http://knoesis.org/library/resource.php?id=1900 City Notifications as a Data Source for Traffic Management] In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, [http://knoesis.org/library/resource.php?id=1818 Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases.] 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9364 2015-12-09T17:47:01Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> <br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta 'Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation', IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, City Notifications as a Data Source for Traffic Management In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9363 2015-12-09T17:46:27Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ====Heterogeneity====<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ====Complexity of Interactions====<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ====Uncertainty of Interactions====<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ====Dynamism====<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta 'Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation', IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, City Notifications as a Data Source for Traffic Management In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9362 2015-12-09T17:46:05Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ===City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System===<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ===Challenges in Processing Physical-Cyber-Social Data===<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta 'Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation', IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, City Notifications as a Data Source for Traffic Management In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9361 2015-12-09T17:45:38Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we have exlplored Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We have utilized the city traffic events extracted from text to explain anomalous dynamics. We carried out a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results [Anantharam-16].<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta 'Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation', IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, City Notifications as a Data Source for Traffic Management In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9360 2015-12-09T17:43:27Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. We show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we have explored the use of Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta 'Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation', IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, City Notifications as a Data Source for Traffic Management In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9359 2015-12-09T17:41:53Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [Anantharam-15].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. In this paper, we show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, we use Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights [Anantharam-13b, Anantharam-14]. <br /> <br /> ===City Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [Anantharam-13].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta 'Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation', IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, City Notifications as a Data Source for Traffic Management In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9358 2015-12-09T17:38:15Z

<p>Pramod: /* Publications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===City Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-14] Pramod Anantharam, Biplav Srivastava, Raj Gupta 'Dynamic Update of Public Transport Schedules in Cities Lacking Traffic Instrumentation', IBM Research Technical Report 2014.<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, City Notifications as a Data Source for Traffic Management In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9357 2015-12-09T17:36:54Z

<p>Pramod: /* Publications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===City Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-13b] Pramod Anantharam and Biplav Srivastava, City Notifications as a Data Source for Traffic Management In Proceedings of the 20th ITS World Congress 2013, October 14-18, 2013, Tokyo, Japan.<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9356 2015-12-09T17:35:34Z

<p>Pramod: /* Publications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===City Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[Anantharam-16] Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee. (2016) Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations. at The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12--17, Phoenix, Arizona, USA (accepted)<br /> *[Anantharam-15] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[Anantharam-13] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9355 2015-12-09T17:34:06Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===City Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===City Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9354 2015-12-09T17:33:15Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Goals==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9353 2015-12-09T17:32:37Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Challenges in Processing Physical-Cyber-Social Data==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9352 2015-12-09T17:30:19Z

<p>Pramod: /* Dynamism */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ===Dynamism===<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9351 2015-12-09T17:30:01Z

<p>Pramod: /* Problem Challenges */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Dynamism==<br /> Traffic conditions and events influencing traffic evolve continuously resulting in challenges for understanding traffic dynamics. Traffic dynamics refers to the variations in speed and travel time observations over time and understanding traffic dynamics refers to explaining these dynamics utilizing events reported on social data stream. We need algorithms that allow us to generate explanations for varying traffic dynamics.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9350 2015-12-09T17:22:55Z

<p>Pramod: /* City as a Physical-Cyber-Social (PCS) System */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have machine sensors monitoring the Physical world reporting their observations on the Cyber world. There are citizens in a city reporting their observations (Social) of various city related activities. Real-world events manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of the interactions in the physical world. We require the analytics algorithms to consider observations of all the three aspects (PCS) for a better understanding of the physical world leading to informed actions by citizens and city authorities.<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9349 2015-12-09T17:18:51Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure. Specifically, extracting and understanding traffic infrastructure related events is explored due to the availability of open data.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9348 2015-12-09T17:16:39Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> People are central to any city and their reports of city infrastructure related observations provides unprecedented opportunities to gain insights into the working of a city. [http://knoesis.org/ Kno.e.sis] as a partner leads the effort to leverage observations from people in a city to understand various aspects of city infrastructure.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9347 2015-12-09T17:09:04Z

<p>Pramod: </p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Overview==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9346 2015-12-09T17:08:26Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely information for informed decision making. <br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Traffic Management==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9345 2015-12-09T17:08:07Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of [http://www.ict-citypulse.eu/page/content/consortium nine partners] spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in CityPulse project. CityPulse aspires to provide an infrastructure for real-time processing of massive multimodal observations from a city allowing citizens, city authorities, and developers to exploit this data to obtain timely and insightful information. <br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Traffic Management==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9344 2015-12-09T16:32:52Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> <br /> <br /> CityPulse is a EU FP7 project consisting of a consortia of nine partners spanning academia, industry, and city partners. [http://knoesis.org/ Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] is a partner in the EU FP7 Citypulse project, a [http://www.ict-citypulse.eu/page/content/consortium consortium] of eight other research partners in the Europe.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Traffic Management==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9343 2015-12-09T16:26:57Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> &quot;CityPulse provides innovative smart city applications by adopting an integrated approach to the Internet of Things and the Internet of People. The project will facilitate the creation and provision of reliable real-time smart city applications by bringing together the two disciplines of knowledge-based computing and reliability testing.&quot;<br /> <br /> [http://knoesis.org/ Kno.e.sis] is a partner in the EU FP7 Citypulse project, a [http://www.ict-citypulse.eu/page/content/consortium consortium] of eight other research partners in the Europe.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Traffic Management==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9342 2015-12-09T16:26:25Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> &quot;CityPulse provides innovative smart city applications by adopting an integrated approach to the Internet of Things and the Internet of People. The project will facilitate the creation and provision of reliable real-time smart city applications by bringing together the two disciplines of knowledge-based computing and reliability testing.&quot;<br /> <br /> Kno.e.sis is a partner in the EU FP7 Citypulse project, a [http://www.ict-citypulse.eu/page/content/consortium consortium] of eight other research partners in the Europe.<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Traffic Management==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9341 2015-12-09T16:21:55Z

<p>Pramod: /* CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> &quot;CityPulse provides innovative smart city applications by adopting an integrated approach to the Internet of Things and the Internet of People. The project will facilitate the creation and provision of reliable real-time smart city applications by bringing together the two disciplines of knowledge-based computing and reliability testing.&quot;<br /> <br /> CiytPulse [http://www.ict-citypulse.eu/page/ project page]<br /> <br /> ==Traffic Management==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod https://wiki.aiisc.ai/index.php?title=Citypulse&diff=9340 2015-12-09T16:21:05Z

<p>Pramod: /* References */</p> <hr /> <div>==CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications==<br /> &quot;CityPulse provides innovative smart city applications by adopting an integrated approach to the Internet of Things and the Internet of People. The project will facilitate the creation and provision of reliable real-time smart city applications by bringing together the two disciplines of knowledge-based computing and reliability testing.&quot;<br /> <br /> ==Traffic Management==<br /> Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers. <br /> <br /> ==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==<br /> A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).<br /> <br /> ==Problem Challenges==<br /> A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc. <br /> <br /> ===Heterogeneity===<br /> Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams. <br /> <br /> ===Complexity of Interactions===<br /> Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.<br /> <br /> ===Uncertainty of Interactions===<br /> Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --&gt; traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.<br /> <br /> ==Solution Components==<br /> As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).<br /> <br /> We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.<br /> <br /> ===Event Extraction===<br /> Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].<br /> <br /> We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.<br /> <br /> ===Event Understanding===<br /> Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].<br /> <br /> ==Publications==<br /> *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317<br /> *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.</div>

Pramod