Difference between revisions of "Cory Andrew Henson"

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==Research Projects==
 
==Research Projects==
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===Perception and Analysis of Sensor Data===
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=== Semantic Web approach to Machine Perception (i.e., Abstraction) ===
 
Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data; yet we are able to make sense out of our environment with relative ease. This is due, in part, to the bi-directional information flow between our sensory organs and analytical brain. Drawing inspiration from cognitive models of perception, we can improve machine perception by allowing communication from processes that analyze observations to processes that generate observations. Such a perceptual system provides effective utilization of resources by decreasing the cost and number of observations needed for achieving situation awareness.
 
Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data; yet we are able to make sense out of our environment with relative ease. This is due, in part, to the bi-directional information flow between our sensory organs and analytical brain. Drawing inspiration from cognitive models of perception, we can improve machine perception by allowing communication from processes that analyze observations to processes that generate observations. Such a perceptual system provides effective utilization of resources by decreasing the cost and number of observations needed for achieving situation awareness.
 +
 +
<strong>Selected Publications</strong>
 +
* [http://www.knoesis.org/library/resource.php?id=1633 An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web] (Applied Ontology, 2011)
 +
* [http://knoesis.org/library/resource.php?id=1548 Active Perception Over Machine and Citizen Sensing] (Presentation at SemTech, 2011)
 +
* [http://www.knoesis.org/library/resource.php?id=1546 Representation of Parsimonious Covering Theory in OWL-DL] ([http://knoesis.org/library/resource.php?id=1549 presentation]) (OWLED, 2011)
 +
<br/>
  
 
===Semantic Sensor Web===
 
===Semantic Sensor Web===
 
Millions of sensors around the globe currently collect avalanches of data about our environment. The rapid development and deployment of sensor technology involves many different types of sensors, both remote and in situ, with such diverse capabilities as range, modality, and maneuverability. It is possible today to utilize networks with multiple sensors to detect and identify objects of interest up close or from a great distance. The lack of integration and communication between these networks, however, often leaves this avalanche of data stovepiped and intensifies the existing problem of too much data and not enough knowledge. With a view to alleviating this glut, we propose that sensor data be annotated with semantic metadata to provide contextual information essential for situational awareness. In particular, we present an approach to annotating sensor data with spatial, temporal, and thematic semantic metadata. This technique builds on current standardization efforts within the W3C and Open Geospatial Consortium (OGC) and extends them with semantic Web technologies to provide enhanced descriptions and access to sensor data.
 
Millions of sensors around the globe currently collect avalanches of data about our environment. The rapid development and deployment of sensor technology involves many different types of sensors, both remote and in situ, with such diverse capabilities as range, modality, and maneuverability. It is possible today to utilize networks with multiple sensors to detect and identify objects of interest up close or from a great distance. The lack of integration and communication between these networks, however, often leaves this avalanche of data stovepiped and intensifies the existing problem of too much data and not enough knowledge. With a view to alleviating this glut, we propose that sensor data be annotated with semantic metadata to provide contextual information essential for situational awareness. In particular, we present an approach to annotating sensor data with spatial, temporal, and thematic semantic metadata. This technique builds on current standardization efforts within the W3C and Open Geospatial Consortium (OGC) and extends them with semantic Web technologies to provide enhanced descriptions and access to sensor data.
  
−
===Semantic Modeling and Annotation of Sensor Data===
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<strong>Selected Publications</strong>
−
Ontologies and other semantic technologies can be key enabling technologies for sensor networks because they will improve semantic interoperability and intergration, as well as facilitate reasoning, classification and other types of assurance and automation not included in the OGC standards. A semantic sensor network will allow the network, its sensors and the resulting data to be organised, installed and managed, queried, understood and controlled through high-level specifications. Ontologies for sensors will provide a framework for describing sensors. These ontologies will allow classification and reasoning on the capabilities and measurements of sensors, provenance of measurements and may allow reasoning about individual sensors as well as reasoning about the connection of a number of sensors as a macroinstrument. The sensor ontologies will, to some degree, reflect the OGC standards and, given ontologies that can encode sensor descriptions, understanding how to map between the ontologies and OGC models is an important consideration. Semantic annotation of sensor descriptions and services that support sensor data exchange and sensor network management will serve a similar purpose as that espoused by semantic annotation of Web services. This research is conducted through the [http://www.w3.org/2005/Incubator/ssn/charter W3C Semantic Sensor Network Incubator Group (SSN-XG)] activity.
+
* [http://www.knoesis.org/library/resource.php?id=1635 Semantic Sensor Network XG Final Report] (W3C Incubator Group Report, 2011)
 +
*  [http://knoesis.wright.edu/library/resource.php?id=00596 SemSOS: Semantic Sensor Observation Service] (CTS, 2009)
 +
* [http://knoesis.wright.edu/library/resource.php?id=00311 Semantic Sensor Web] (IEEE Internet Computing, 2008)
 +
<br/>
  
 
===Trust on Semantic Sensor Web===
 
===Trust on Semantic Sensor Web===
 
Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this work, we briefly review existing work on trust networks, pointing out some of its drawbacks. We then propose a local framework to explore two different kinds of trust among agents called referral trust and functional trust, that are modelled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.
 
Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this work, we briefly review existing work on trust networks, pointing out some of its drawbacks. We then propose a local framework to explore two different kinds of trust among agents called referral trust and functional trust, that are modelled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.
  
−
===Analysis of Streaming Sensor Data===
+
<strong>Selected Publications</strong>
−
Sensors are increasingly being deployed for continuous monitoring of physical phenomena, resulting in avalanche of sensor data. Current sensor data streams provide summaries (e.g., min., max., avg.) of how phenomena change over time; however, such summaries are of little value to decision makers attempting to attain an insight or an intuitive awareness of the situation. Feature-streams, on the other hand, provide a higher-level of abstraction over the sensor data and provide actionable knowledge useful to the decision maker. This work presents an approach to generate feature-streams in real-time. This is accomplished through the application of ontological domain knowledge in order to integrate multiple, multimodal, heterogeneous low-level sensor data streams and infer the existence of real-world events like Blizzard, RainStorm etc. The generated feature-streams are publicly accessible on the Linked Open Data (LOD) Cloud.
+
*  [http://knoesis.wright.edu/library/resource.php?id=798 Some Trust Issues in Social Networks and Sensor Networks] (CTS, 2010)
−
<br/><br/>
+
*  [http://knoesis.wright.edu/library/resource.php?id=00474 A Local Qualitative Approach to Referral and Functional Trust] (IICAI, 2009)
 +
<br/>
  
 
==Publications==
 
==Publications==
 
[http://scholar.google.com/citations?hl=en&user=vdvzlBYAAAAJ Google Scholar Index]
 
[http://scholar.google.com/citations?hl=en&user=vdvzlBYAAAAJ Google Scholar Index]
  
−
===2011===
+
<strong>2011</strong>
 
* [http://www.knoesis.org/library/resource.php?id=1633 An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web]
 
* [http://www.knoesis.org/library/resource.php?id=1633 An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web]
 
** Cory Henson, Krishnaprasad Thirunarayan, and Amit Sheth
 
** Cory Henson, Krishnaprasad Thirunarayan, and Amit Sheth
−
** in Applied Ontology, 2011.
+
** Applied Ontology, 2011.
 
* [http://www.knoesis.org/library/resource.php?id=1631 SECURE: Semantics Empowered resCUe Environment (Demonstration Paper)] ([http://www.youtube.com/watch?v=gHn9aCt9zQU demo])
 
* [http://www.knoesis.org/library/resource.php?id=1631 SECURE: Semantics Empowered resCUe Environment (Demonstration Paper)] ([http://www.youtube.com/watch?v=gHn9aCt9zQU demo])
 
** Pratikkumar Desai, Cory Henson, Pramod Anantharam, Amit Sheth
 
** Pratikkumar Desai, Cory Henson, Pramod Anantharam, Amit Sheth
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** Kno.e.sis Technical Report, 2011.
 
** Kno.e.sis Technical Report, 2011.
  
−
===2010===
+
<strong>2010</strong>
 
* [http://knoesis.wright.edu/library/resource.php?id=836 Trust Model for Semantic Sensor and Social Networks: A Preliminary Report]
 
* [http://knoesis.wright.edu/library/resource.php?id=836 Trust Model for Semantic Sensor and Social Networks: A Preliminary Report]
 
** Pramod Anantharam,Cory Henson,Krishnaprasad Thirunarayan and Amit Sheth
 
** Pramod Anantharam,Cory Henson,Krishnaprasad Thirunarayan and Amit Sheth
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** Kno.e.sis Technical Report, 2010.
 
** Kno.e.sis Technical Report, 2010.
  
−
===2009===
+
<strong>2009</strong>
 
* [http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-522/p6.pdf A Survey of the Semantic Specification of Sensors]
 
* [http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-522/p6.pdf A Survey of the Semantic Specification of Sensors]
 
** Michael Compton, Cory Henson, Laurent Lefort, Holger Neuhaus, and Amit Sheth
 
** Michael Compton, Cory Henson, Laurent Lefort, Holger Neuhaus, and Amit Sheth
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** 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009)
 
** 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009)
  
−
===2008===
+
<strong>2008 (and earlier)</strong>
 
* [http://knoesis.wright.edu/library/resource.php?id=00311 Semantic Sensor Web]
 
* [http://knoesis.wright.edu/library/resource.php?id=00311 Semantic Sensor Web]
 
** Amit Sheth, Cory Henson, and Satya Sahoo
 
** Amit Sheth, Cory Henson, and Satya Sahoo
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== Professional Activities ==
 
== Professional Activities ==
−
=== Standards Committee Member ===
+
<strong>Invited Talks</strong>
−
* SSN-XG: W3C Semantic Sensor Network Incubator Group [http://www.w3.org/2005/Incubator/ssn/XGR-ssn/ Final Report]
+
* Dagstuhl Seminar, [http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=10042 Semantic Challenges in Sensor Networks (2010)]: [http://knoesis.wright.edu/library/resource.php?id=769 Can Sensors Play 20 Questions?]
 +
<br/>
 +
 
 +
<strong>Standards Committee Member</strong>
 +
* Editor for the W3C Semantic Sensor Network Incubator Group (SSN XG): [http://www.w3.org/2005/Incubator/ssn/XGR-ssn/ Final Report, 2011]
 +
<br/>
  
−
=== Program Committee Member ===
+
<strong>Program Committee Member</strong>
 
* ESWC 2012: The 2012 Extended Semantic Web Conference
 
* ESWC 2012: The 2012 Extended Semantic Web Conference
 
* CogSIMA 2012: IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support
 
* CogSIMA 2012: IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support
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<br/>
 
<br/>
  
−
== About Me ==
+
== Education and Work Experience ==
−
=== Education ===
+
<strong>Education</strong>
 
* Ph.D. Candidate, Computer Science and Engineering, Wright State University, January 2007 - Present
 
* Ph.D. Candidate, Computer Science and Engineering, Wright State University, January 2007 - Present
 
* B.A., Cognitive Science, University of Georgia, August 2005
 
* B.A., Cognitive Science, University of Georgia, August 2005
 
* B.S., Computer Science, University of Georgia, December 2002
 
* B.S., Computer Science, University of Georgia, December 2002
 +
<br/>
  
−
=== Work Experience ===
+
<strong>Work Experience</strong>
 
* Research Assistant (Jan. 2007 - Present): Kno.e.sis -- Ohio Center of Excellence in Knowledge-enabled Computing
 
* Research Assistant (Jan. 2007 - Present): Kno.e.sis -- Ohio Center of Excellence in Knowledge-enabled Computing
 
* Fellowship (2008 - 2011): Sensors Directorate, Air Force Research Lab (AFRL), Wright Patterson Air Force Base (WPAFB)
 
* Fellowship (2008 - 2011): Sensors Directorate, Air Force Research Lab (AFRL), Wright Patterson Air Force Base (WPAFB)
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* Research Assistant (May 2005 - Dec. 2006): Large Scale Distributed Information Systems Laboratory (LSDIS)
 
* Research Assistant (May 2005 - Dec. 2006): Large Scale Distributed Information Systems Laboratory (LSDIS)
 
* Student Worker (Sept. 2004 - May 2005): Complex Carbohydrate Research Center (CCRC)
 
* Student Worker (Sept. 2004 - May 2005): Complex Carbohydrate Research Center (CCRC)
 +
<br/>

Revision as of 15:03, 24 November 2011

Cah.jpeg

Researcher - Kno.e.sis - Ohio Center of Excellence in Knowledge-enabled Computing
Ph.D. Candidate - Wright State University, Advisor Dr. Amit Sheth

Look at the image to the right. How quickly were you able to realize the identity of the depicted object? The activity you just engaged in is called perception; and while people are able to perceive their environment almost instantaneously, and seemingly without effort, machines continue to struggle with the task. Currently, I am investigating how people are able to perceive the world so effectively; and developing an approximation of this process that can be formalized to better enable machines to perceive. This formalization has resulted in an ontology of perception, IntellegO, which is now being used in several distinct projects, including a weather alert service, fire detecting robots, and health-care monitoring (cardiology).

While completing my Ph.D. in Computer Science, I also act as Project Lead for the Semantic Sensor Web and editor for the W3C Semantic Sensor Network Incubator Group.

Research Projects

Semantic Web approach to Machine Perception (i.e., Abstraction)

Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data; yet we are able to make sense out of our environment with relative ease. This is due, in part, to the bi-directional information flow between our sensory organs and analytical brain. Drawing inspiration from cognitive models of perception, we can improve machine perception by allowing communication from processes that analyze observations to processes that generate observations. Such a perceptual system provides effective utilization of resources by decreasing the cost and number of observations needed for achieving situation awareness.

Selected Publications


Semantic Sensor Web

Millions of sensors around the globe currently collect avalanches of data about our environment. The rapid development and deployment of sensor technology involves many different types of sensors, both remote and in situ, with such diverse capabilities as range, modality, and maneuverability. It is possible today to utilize networks with multiple sensors to detect and identify objects of interest up close or from a great distance. The lack of integration and communication between these networks, however, often leaves this avalanche of data stovepiped and intensifies the existing problem of too much data and not enough knowledge. With a view to alleviating this glut, we propose that sensor data be annotated with semantic metadata to provide contextual information essential for situational awareness. In particular, we present an approach to annotating sensor data with spatial, temporal, and thematic semantic metadata. This technique builds on current standardization efforts within the W3C and Open Geospatial Consortium (OGC) and extends them with semantic Web technologies to provide enhanced descriptions and access to sensor data.

Selected Publications


Trust on Semantic Sensor Web

Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this work, we briefly review existing work on trust networks, pointing out some of its drawbacks. We then propose a local framework to explore two different kinds of trust among agents called referral trust and functional trust, that are modelled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.

Selected Publications


Publications

Google Scholar Index

2011

2010

2009

2008 (and earlier)


Professional Activities

Invited Talks


Standards Committee Member


Program Committee Member

  • ESWC 2012: The 2012 Extended Semantic Web Conference
  • CogSIMA 2012: IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support
  • GEOProcessing 2012: The 4th International Conference on Advanced Geographic Information Systems, Applications, and Services
  • SEMAPRO 2011: International Conference on Advances in Semantic Processing
  • SSN 2011: The 2011 International Semantic Web Conference (ISWC 2011), 4rd International Workshop on Semantic Sensor Networks
  • SESA 2011: The 12th International Conference on Distributed Computing and Networking (ICDCN 2011), Workshop on Sensor-Enabled Situational Awareness
  • ESWC 2011: The 2011 Extended Semantic Web Conference
  • GEOProcessing 2011: The Third International Conference on Advanced Geographic Information Systems, Applications, and Services
  • SSN 2010: The 2010 International Semantic Web Conference (ISWC 2010), 3rd International Workshop on Semantic Sensor Networks
  • SWE 2010: The 2010 International Symposium on Collaborative Technologies and Systems (CTS 2009), Workshop on Sensor Web Enablement
  • ESWC 2010: The 2010 Extended Semantic Web Conference
  • SSN 2009: The 2009 International Semantic Web Conference (ISWC 2009), 2nd International Workshop on Semantic Sensor Networks
  • GeoS 2009: The Third International Conference on Geospatial Semantics
  • SWE 2009: The 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009), Workshop on Sensor Web Enablement


Education and Work Experience

Education

  • Ph.D. Candidate, Computer Science and Engineering, Wright State University, January 2007 - Present
  • B.A., Cognitive Science, University of Georgia, August 2005
  • B.S., Computer Science, University of Georgia, December 2002


Work Experience

  • Research Assistant (Jan. 2007 - Present): Kno.e.sis -- Ohio Center of Excellence in Knowledge-enabled Computing
  • Fellowship (2008 - 2011): Sensors Directorate, Air Force Research Lab (AFRL), Wright Patterson Air Force Base (WPAFB)
  • Internship (June - Aug. 2010): National MASINT Office (NMO), Defense Intelligence Agency (DIA)
  • Internship (Jan. - March 2009): GRIDS Lab (University of Melbourne), and CSIRO Tasmanian ICT Center
  • Research Assistant (May 2005 - Dec. 2006): Large Scale Distributed Information Systems Laboratory (LSDIS)
  • Student Worker (Sept. 2004 - May 2005): Complex Carbohydrate Research Center (CCRC)


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