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Situational Awareness Technologies for Disaster Response

  • Naveen Ashish
  • Ronald Eguchi
  • Rajesh Hegde
  • Charles Huyck
  • Dmitri Kalashnikov
  • Sharad Mehrotra
  • Padhraic Smyth
  • Nalini Venkatasubramanian
Part of the Integrated Series In Information Systems book series (ISIS, volume 18)

This chapter highlights some of the key information technology challenges being addressed in the RESCUE project, a National Science Foundation (NSF) funded 5-year effort, with a particular focus on situational awareness technologies. A key premise of the project is that the critical decision making required in disaster situations relies heavily on the availability, accuracy, and timeliness of information that can be made available to the decision makers. A major thrust within RESCUE is focusing on developing next generation situational awareness technologies. Our approach in building situational awareness systems is to build information systems that consider situations and events as fundamental entities, and our research is focused on the key technical challenges in the extraction and synthesis, management, and analysis of such situational information. This chapter focuses on our research accomplishments in each of these areas and also provides an overview of technology transition activities.

Keywords

Situational Awareness Disaster Response Federal Emergency Management Agency Event Extraction Spatial Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Sharad Mehrotra, Carter Butts, Dmitri Kalashnikov, Nalini Venkatasubramanian, Ramesh Rao, Ganz Chockalingam, Ron Eguchi, Beverly Adams and Charles Huyck. Project Rescue: Challenges in Responding to the Unexpected. IS&T/SPIE 16th Annual Symposium on Electronic Imaging. January 18-22 2004, San Jose, California, USA, 2003-12.Google Scholar
  2. [2]
    Dmitri Kalashnikov, Dawit Seid, Yiming Ma, Naveen Ashish, Sharad Mehrotra and Nalini Venkatasubramanian. Event Based Approach to Situational Awareness. Calit2 Report, UC Irvine.Google Scholar
  3. [3]
    Zhaoqi Chen, Dmitri V. Kalashnikov, Sharad Mehrotra: Exploiting relationships for object consolidation. IQIS 2005: 47-58.Google Scholar
  4. [4]
    Dmitri V. Kalashnikov, Sharad Mehrotra, Zhaoqi Chen: Exploiting Relationships for Domain-Independent Data Cleaning. SDM 2005.Google Scholar
  5. [5]
    Reynold Cheng, Dmitri V. Kalashnikov, and Sunil Prabhakar. Evaluating probabilistic queries over imprecise data. In Proc. of ACM SIGMOD Int'l Conf. on Management of Data (ACM SIGMOD), June 9-12, 2003.Google Scholar
  6. [6]
    Wenyi Zhang and Bhaskar Rao. Robust Broadband Beamformer with Diagonally Loaded Contraint Matrix and its Application to Speech Recognition, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-2006, Toulose, France.Google Scholar
  7. [7]
    Wenyi Zhang and Bhaskar Rao. Robust Adaptive Beamformer with Feasibility Constraint on the Steering Vector, European Signal Processing Conference, EUSPCO-2006, Italy.Google Scholar
  8. [8]
    S. Park, M. M. Trivedi. A Track-based Human Movement Analysis and Privacy Protection System Adaptive to Environmental Contexts. Proc. IEEE International Conference on Advanced Video and Signal based Surveillance, Sep 2005.Google Scholar
  9. [9]
    H. Cunningham, D. Maynard, K. Bontcheva, V. Tablan. GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL'02). Philadelphia, July 2002.Google Scholar
  10. [10]
    K. Erk and S. Pado. Shalmaneser - a flexible toolbox for semantic role assignment. Proceedings of LREC-06, Genoa.Google Scholar
  11. [11]
    Dan Klein and Christopher D. Manning. Fast Exact Inference with a Factored Model for Natural Language Parsing. In Advances in Neural Information Processing Systems 15 (NIPS 2002), December 2002.Google Scholar
  12. [12]
    Bo Gong, Utz Westermann, Srikanth Agaram and Ramesh Jain. Event Discovery in Multimedia Reconnaissance Data Using Event Clustering. AAAI Workshop on Event Extraction and Synthesis, Boston MA, July 2006.Google Scholar
  13. [13]
    Dmitri V. Kalashnikov, Yiming Ma, Sharad Mehrotra, Ram Hariharan, Nalini Venkatasubramanian, and Naveen Ashish. SAT: Spatial Awareness from Textual Input. In Proc. of Int'l Conf. on Extending Database Technology (EDBT), demo publication, March 26-30, 2006.Google Scholar
  14. [14]
    S. Mehrotra, C. Butts, D. Kalashnikov, N. Venkatasubramanian, K. Altintas, et al. CAMAS: A Citizen Awareness System for Crisis Mitigation. In Proc. of ACM SIGMOD Int'l Conf. on Management of Data (ACM SIGMOD), demo publication, June 13-18, 2004.Google Scholar
  15. [15]
    Dmitri V. Kalashnikov, Yiming Ma, Sharad Mehrotra, and Ram Hariharan. Index for Fast Retrieval of Uncertain Spatial Point Data. In Proc. of Int'l Symposium on Advances in Geographic Information Systems (ACM GIS), November 10-11, 2006.Google Scholar
  16. [16]
    Dmitri V. Kalashnikov, Yiming Ma, Sharad Mehrotra, and Ram Hariharan. Modeling and Querying Uncertain Spatial Information for Situational Awareness Applications. In Proc. of Int'l Symposium on Advances in Geographic Information Systems (ACM GIS), November 10-11, 2006.Google Scholar
  17. [17]
    Dawit Seid and Sharad Mehrotra. Algebraic Support and Optimization for Semantic Queries. RESCUE technical report, 2006.Google Scholar
  18. [18]
    Dawit Yimam Seid and Sharad Mehrotra. Efficient Relationship Pattern Mining Using Multi-Relational Iceberg-Cubes. ICDM, 2004, pp. 515-518.Google Scholar
  19. [19]
    Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt and James Marca. Modeling Transportation Routines using Hybrid Dynamic Mixed Networks. In Uncertainty in Artificial Intelligence 2005.Google Scholar
  20. [20]
    Vibhav Gogate and Rina Dechter. Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints. In Uncertainty in Artificial Intelligence 2005.Google Scholar
  21. [21]
    Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, and James Marca. Model-ing Travel and Activity Routines using Hybrid Dynamic Mixed Networks. In 85th annual meeting of the Transportation Research Board, 2006.Google Scholar
  22. [22]
    Dmitri V. Kalashnikov, Sharad Mehrotra. Domain-independent data cleaning via analysis of entity-relationship graph. ACM Transactions on Database Systems 31(2): 716-767 (2006).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Naveen Ashish
    • 1
  • Ronald Eguchi
    • 2
  • Rajesh Hegde
    • 3
  • Charles Huyck
    • 2
  • Dmitri Kalashnikov
    • 1
  • Sharad Mehrotra
    • 1
  • Padhraic Smyth
    • 1
  • Nalini Venkatasubramanian
    • 1
  1. 1.Donald Bren School of Information and Computer SciencesUniversity of CaliforniaIrvineUSA
  2. 2.Image Cat, Inc.Long BeachUSA
  3. 3.Department of Electrical and Computer EngineeringUniversity of CaliforniaSan DiegoUSA

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