Hazard Sees - Revision history

Pavan: Redirected page to Social and Physical Sensing Enabled Decision Support

2015-09-29T20:00:45Z

Redirected page to Social and Physical Sensing Enabled Decision Support

Pavan: /* Key Questions */

2015-08-09T18:33:09Z

‎Key Questions

Pavan: /* People */

2015-08-09T18:27:27Z

‎People

Pavan: /* People */

2015-08-09T18:26:50Z

‎People

Pavan: /* Key Questions */

2015-08-09T18:10:31Z

‎Key Questions

Pavan: /* Overview */

2015-08-09T18:10:05Z

‎Overview

Pavan at 16:34, 9 August 2015

2015-08-09T16:34:27Z

Pavan: /* Summary */

2015-08-09T16:26:03Z

‎Summary

Pavan: Created page with "=Summary= Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. K..."

2015-08-09T16:25:46Z

Created page with "=Summary= Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. K..."

New page

=Summary=
Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. Katrina, Sandy) or a flood (e.g. Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project seeks to radically reform decision support systems for managing rapidly changing disaster situations by the integrated exploitation of social, physical and hazard modeling capabilities. This effort is expected to provide a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences and the social sciences for research, education, and training of undergraduate and graduate students including those from under-represented groups.

The team will design novel, multi-dimensional cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. By assimilating data from social and physical sensors and their integrated modeling and analysis, methodology to predict and help prioritize the temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g. bridges, traffic signals) will be designed. The team will develop innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification, correlation or integration of relevant data across multiple sources and modalities (social, physical and Web). Novel coupling of socio-linguistic and network analysis will be used to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and their impact on hazard models (e.g. storm surge) and flood mapping. Domain-grounded mechanisms will be developed to address pervasive trustworthiness and reliability concerns. Exemplar outcomes are expected to include specific tools for first-responders as well as recovery teams to aid in the prioritization of relief and repair efforts, leveraging improved flood response, urban mapping, and dynamic storm surge models, and interdisciplinary training of students leveraging research in pedagogy, in conjunction with Ohio State University’s new undergraduate major in data analytics, and Wright State University’s Big and Smart Data graduate certificate program.

=People=
*Principal Investigators: [http://web.cse.ohio-state.edu/~srini/ Srinivasan Parthasarathy (OSU)], [http://knoesis.wright.edu/amit Amit P. Sheth (Kno.e.sis)]
*Graduate Students: [http://knoesis.org/researchers/pavan Pavan Kapanipathi], [http://knoesis.org/researchers/sarasi Sarasi Lalithsena]

=Related Projects=
[http://knoesis.org/projects/socs SoCS]

@@ Line 1: / Line 1: @@
-Hazard Sees is a NSF funded, collaborative project between [http://knoesis.org Kno.e.sis Center, Wright State University] and [https://www.osu.edu/ Ohio State University].
+#REDIRECT [[Social_and_Physical_Sensing_Enabled_Decision_Support]]
-=Overview=
-Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. Katrina, Sandy) or a flood (e.g. Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project seeks to radically reform decision support systems  for managing rapidly changing  disaster situations by the integrated exploitation of social, physical and hazard modeling capabilities. This effort is expected to provide a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences and the social sciences for research, education, and training of undergraduate and graduate students including those from under-represented groups.
-−
-The team will design novel, multi-dimensional cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. By assimilating data from social and physical sensors and their integrated modeling and analysis, methodology to predict and help prioritize the temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g. bridges, traffic signals) will be designed.  The team will develop innovative technology to support the identification of new background knowledge and structured data to  improve object  extraction, location identification, correlation or integration of relevant data across multiple sources and modalities (social, physical and Web). Novel coupling of socio-linguistic and network analysis will be used to identify important persons and objects, statistical and factual  knowledge about traffic and transportation networks, and their impact on hazard models (e.g. storm surge) and  flood mapping. Domain-grounded mechanisms will be developed to address pervasive trustworthiness and reliability concerns. Exemplar outcomes are expected to include specific tools for  first-responders as well as recovery teams to aid in the prioritization of relief and repair efforts, leveraging improved flood response, urban mapping, and dynamic storm surge models, and interdisciplinary training of students leveraging research in pedagogy, in conjunction with Ohio State University’s new undergraduate major in data analytics, and Wright State University’s Big and Smart Data graduate certificate program.
-−
-==Research Questions==
-Key questions that will be addressed in this project are:
-# How can we extract reliable, trustworthy and relevant nuggets of information related to civil infrastructure from language-based citizen sensed data sets across the stages of the disaster life-cycle (preparedness, response & recovery)?
-# How can we develop adaptive and dynamic models of hurricane storm surge and flood resiliency coupled with fused information from citizen sensed and remote sensed data?
-# What kind of interface and tools can assist first-responders in leveraging physical sensing (e.g. remote sensing), citizen sensing and their interactions pertaining to infrastructure elements (e.g. chokepoints in a traffic network due to road or bridge closures).
-−
-=People=
-*Principal Investigators:  [http://web.cse.ohio-state.edu/~srini/ Srinivasan Parthasarathy (OSU)], [http://knoesis.wright.edu/amit Amit P. Sheth (Kno.e.sis)]
-*Co-Principal Investigators:  Densheng Liu (OSU), Valerie Shalin (WSU), [http://knoesis.wright.edu/tkprasad/ T.K.Prasad (WSU)]
-*Graduate Students: [http://knoesis.org/researchers/pavan Pavan Kapanipathi], [http://knoesis.org/researchers/sarasi Sarasi Lalithsena]
-−
-=Related Projects=
-[http://knoesis.org/projects/socs SoCS]

@@ Line 5: / Line 5: @@
 The team will design novel, multi-dimensional cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. By assimilating data from social and physical sensors and their integrated modeling and analysis, methodology to predict and help prioritize the temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g. bridges, traffic signals) will be designed.  The team will develop innovative technology to support the identification of new background knowledge and structured data to  improve object  extraction, location identification, correlation or integration of relevant data across multiple sources and modalities (social, physical and Web). Novel coupling of socio-linguistic and network analysis will be used to identify important persons and objects, statistical and factual  knowledge about traffic and transportation networks, and their impact on hazard models (e.g. storm surge) and  flood mapping. Domain-grounded mechanisms will be developed to address pervasive trustworthiness and reliability concerns. Exemplar outcomes are expected to include specific tools for  first-responders as well as recovery teams to aid in the prioritization of relief and repair efforts, leveraging improved flood response, urban mapping, and dynamic storm surge models, and interdisciplinary training of students leveraging research in pedagogy, in conjunction with Ohio State University’s new undergraduate major in data analytics, and Wright State University’s Big and Smart Data graduate certificate program.
-==Key Questions==
+==Research Questions==
 Key questions that will be addressed in this project are:
 # How can we extract reliable, trustworthy and relevant nuggets of information related to civil infrastructure from language-based citizen sensed data sets across the stages of the disaster life-cycle (preparedness, response & recovery)?

@@ Line 7: / Line 7: @@
 ==Key Questions==
 Key questions that will be addressed in this project are:
-# How can we extract reliable, trustworthy and relevant nuggets of information related to civil infrastructure
+# How can we extract reliable, trustworthy and relevant nuggets of information related to civil infrastructure from language-based citizen sensed data sets across the stages of the disaster life-cycle (preparedness, response & recovery)?
-from language-based citizen sensed data sets across the stages of the disaster life-cycle (preparedness,
+# How can we develop adaptive and dynamic models of hurricane storm surge and flood resiliency coupled with fused information from citizen sensed and remote sensed data?
-response & recovery)?
+# What kind of interface and tools can assist first-responders in leveraging physical sensing (e.g. remote sensing), citizen sensing and their interactions pertaining to infrastructure elements (e.g. chokepoints in a traffic network due to road or bridge closures).
 # How can we develop adaptive and dynamic models of hurricane storm surge and flood resiliency coupled
 with fused information from citizen sensed and remote sensed data?
 # What kind of interface and tools can assist first-responders in leveraging physical sensing (e.g. remote
 sensing), citizen sensing and their interactions pertaining to infrastructure elements (e.g. chokepoints in a
 traffic network due to road or bridge closures).
 =People=

← Older revision		Revision as of 18:10, 9 August 2015
Line 4:		Line 4:

	The team will design novel, multi-dimensional cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. By assimilating data from social and physical sensors and their integrated modeling and analysis, methodology to predict and help prioritize the temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g. bridges, traffic signals) will be designed. The team will develop innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification, correlation or integration of relevant data across multiple sources and modalities (social, physical and Web). Novel coupling of socio-linguistic and network analysis will be used to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and their impact on hazard models (e.g. storm surge) and flood mapping. Domain-grounded mechanisms will be developed to address pervasive trustworthiness and reliability concerns. Exemplar outcomes are expected to include specific tools for first-responders as well as recovery teams to aid in the prioritization of relief and repair efforts, leveraging improved flood response, urban mapping, and dynamic storm surge models, and interdisciplinary training of students leveraging research in pedagogy, in conjunction with Ohio State University’s new undergraduate major in data analytics, and Wright State University’s Big and Smart Data graduate certificate program.		The team will design novel, multi-dimensional cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. By assimilating data from social and physical sensors and their integrated modeling and analysis, methodology to predict and help prioritize the temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g. bridges, traffic signals) will be designed. The team will develop innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification, correlation or integration of relevant data across multiple sources and modalities (social, physical and Web). Novel coupling of socio-linguistic and network analysis will be used to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and their impact on hazard models (e.g. storm surge) and flood mapping. Domain-grounded mechanisms will be developed to address pervasive trustworthiness and reliability concerns. Exemplar outcomes are expected to include specific tools for first-responders as well as recovery teams to aid in the prioritization of relief and repair efforts, leveraging improved flood response, urban mapping, and dynamic storm surge models, and interdisciplinary training of students leveraging research in pedagogy, in conjunction with Ohio State University’s new undergraduate major in data analytics, and Wright State University’s Big and Smart Data graduate certificate program.
		+
		+	==Key Questions==
		+	Key questions that will be addressed in this project are:
		+	# How can we extract reliable, trustworthy and relevant nuggets of information related to civil infrastructure
		+	from language-based citizen sensed data sets across the stages of the disaster life-cycle (preparedness,
		+	response & recovery)?
		+	# How can we develop adaptive and dynamic models of hurricane storm surge and flood resiliency coupled
		+	with fused information from citizen sensed and remote sensed data?
		+	# What kind of interface and tools can assist first-responders in leveraging physical sensing (e.g. remote
		+	sensing), citizen sensing and their interactions pertaining to infrastructure elements (e.g. chokepoints in a
		+	traffic network due to road or bridge closures).

	=People=		=People=

← Older revision		Revision as of 16:34, 9 August 2015
Line 1:		Line 1:
		+	Hazard Sees is a NSF funded, collaborative project between [http://knoesis.org Kno.e.sis Center, Wright State University] and [https://www.osu.edu/ Ohio State University].
		+	=Overview=
	Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. Katrina, Sandy) or a flood (e.g. Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project seeks to radically reform decision support systems for managing rapidly changing disaster situations by the integrated exploitation of social, physical and hazard modeling capabilities. This effort is expected to provide a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences and the social sciences for research, education, and training of undergraduate and graduate students including those from under-represented groups.		Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. Katrina, Sandy) or a flood (e.g. Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project seeks to radically reform decision support systems for managing rapidly changing disaster situations by the integrated exploitation of social, physical and hazard modeling capabilities. This effort is expected to provide a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences and the social sciences for research, education, and training of undergraduate and graduate students including those from under-represented groups.

← Older revision		Revision as of 16:26, 9 August 2015
Line 1:		Line 1:
−	~~=Summary=~~
	Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. Katrina, Sandy) or a flood (e.g. Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project seeks to radically reform decision support systems for managing rapidly changing disaster situations by the integrated exploitation of social, physical and hazard modeling capabilities. This effort is expected to provide a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences and the social sciences for research, education, and training of undergraduate and graduate students including those from under-represented groups.		Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. Katrina, Sandy) or a flood (e.g. Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project seeks to radically reform decision support systems for managing rapidly changing disaster situations by the integrated exploitation of social, physical and hazard modeling capabilities. This effort is expected to provide a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences and the social sciences for research, education, and training of undergraduate and graduate students including those from under-represented groups.