Social and Physical Sensing Enabled Decision Support
|Social and Physical Sensing Enabled Decision Support|
|Motto||To provide an effective interface and tools to identify means by which first-responders should prioritize relief efforts both during and after a disaster event|
|Timeline||15 Aug 2015 - 31 July 2019|
|Funding Agency||National Science Foundation|
|Award Number||EAR 1520870|
Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response is a NSF funded project involving a collaboration between Kno.e.sis Center, Wright State University and Ohio State University.
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.
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).
- Principal Investigators: Srinivasan Parthasarathy (OSU-Contact PI), Amit P. Sheth (Kno.e.sis, WSU)
- Co-Principal Investigators: Densheng Liu (OSU), Ethan Kubatko (OSU), Valerie Shalin (Kno.e.sis, WSU), T.K.Prasad (Kno.e.sis, WSU)
- Graduate Researchers: Sarasi Lalithsena, Pavan Kapanipathi, Hussein Al-Olimat, Siva Kumar, Manas Gaur.
- Postdoctoral Researcher: Saeedeh Shekarpour Tanvi Banerjee
- Andrew Hampton, Shreyansh Bhatt, Alan Smith, Jeremy Brunn, Hemant Purohit, Valerie Shalin, John Flach, Amit Sheth. Constructing Synthetic Social Media Stimuli for an Emergency Preparedness Functional Exercise. Proc. of the 14th International Conference on Information Systems for Crisis Response and Management (ISCRAM). Albi, Occitanie Pyrénées-Méditerranée, France, May 2017.
- Amit Sheth and Pavan Kapanipathi (2016). Semantic Filtering for Social Data. IEEE Intelligent Systems. 31 (4).
- J. Liang, D. Ajwani, P. Nicolson, A. Sala, S. Parthasarathy (2016). What Links Alice and Bob? Matching and Ranking Semantic Patterns in Heterogeneous Networks. Proceedings of the 25th International World Wide Web Conference.
- Jiongqian Liang, Peter Jacobs and Srinivasan Parthasarathy (2016). Human Guided Flood Mapping on Satellite Images. ACM SIGKDD Interactive Data Exploration and Analytics.
- Kalpa Gunaratna, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng. (2016). Gleaning Types for Literals in RDF Triples with Application to Entity Summarization.. 13th International Conference, ESWC 2016.
- Nikhita Vedula, Srinivasan Parthasarathy and Valerie Shalin (2016). Predicting Trust Relations Among Users in a Social Network: On the roles of Influence, Cohesion and Valence. ACM SIGKDD WISDOM Proceedings.
- Sujan Perera, Pablo N. Mendes, Adarsh Alex, Amit Sheth, Krishnaprasad Thirunarayan. (2016). Implicit Entity Linking in Tweets. In Extended Semantic Web Conference (ESWC).
- Jacob Ross, Krishnaprasad Thirunarayan (2016). Features for Ranking Tweets Based on Credibility and Newsworthiness. 17th International Conference on Collaboration Technologies and Systems (CTS 2016).
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