ABSTRACT
Crisis Management and Disaster Recovery have gained immense importance in the wake of recent man and nature inflicted calamities. A critical problem in a crisis situation is how to efficiently discover, collect, organize, search and disseminate real-time disaster information. In this paper, we address several key problems which inhibit better information sharing and collaboration between both private and public sector participants for disaster management and recovery. We design and implement a web based prototype implementation of a Business Continuity Information Network (BCIN) system utilizing the latest advances in data mining technologies to create a user-friendly, Internet-based, information-rich service and acting as a vital part of a company's business continuity process. Specifically, information extraction is used to integrate the input data from different sources; the content recommendation engine and the report summarization module provide users personalized and brief views of the disaster information; the community generation module develops spatial clustering techniques to help users build dynamic community in disasters. Currently, BCIN has been exercised at Miami-Dade County Emergency Management.
Supplemental Material
- G. Salton, and M. J. McGill. 1983. Introduction to modern information retrieval. McGraw-Hill. Google ScholarDigital Library
- P.-N. Tan, M. Steinbach, and V. Kumar, 2005. Introduction to Data Mining, Addison-Wesley. Google ScholarDigital Library
- R.E. Anderson, Social impacts of computing: Codes of professional ethics. Social Science Computing Review, 2:453--469, 1992.Google ScholarCross Ref
- K. Saleem, S. Luis, Y. Deng, S.-C. Chen, V. Hristidis, and T. Li. Towards a business continuity information network for rapid disaster recovery. Intenational Digital Government Research Conference. 2008: 107--116. Google ScholarDigital Library
- W.E. Mackay. Ethics, lies and videotape. In Proc. of CHI. 1995. Google ScholarDigital Library
- M. Schwartz. Task force on bias-free language. Guidelines for Bias-Free Writing. Indiana University Press, Bloomington IN, 1995.Google Scholar
- J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. of ICML. 2001. Google ScholarDigital Library
- F. Sha, and F. Pereira. Shallow parsing with conditional random fields. In Proc. of HLT-NAACL. 2003. Google ScholarDigital Library
- C.W. Hsu, and C.J. Lin. A comparison of methods for multiclass support vector machines. IEEE Transaction on Neural Networks, 13(2):415--425, 2002. Google ScholarDigital Library
- D. Wang, L. Zheng, T. Li, and Y. Deng. Evolutionary document summarization for disaster management. In Proc. of SIGIR. 2009. Google ScholarDigital Library
- F.L. Han, C. Peng, and C. Ding. Feature selection based on mutual information: criteria of max-dependency, max-relevance,and min-redundancy. IEEE Transaction on Pattern Analysis and Machine Intelligence, 27:1226--1238, 2005. Google ScholarDigital Library
- O.R. Aaiane, A. Foss, C.-H. Lee, and W. Wang. On data clustering analysis: Scalability, constraints and validation. In Proc. of PAKDD 2002. Google ScholarDigital Library
- J. Han, and M. Kamber. 2006. Data Mining Concepts and Techniques 2nd. Morgan Kaufmann. Google ScholarDigital Library
- M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large databases with noise. In Proc. of KDD. 1996.Google Scholar
- C.-H. Lee. Density-based clustering of spatial data in the presence of physical constraints. Master's thesis, University of Alberta, Edmonton, AB, Canada, July 2002.Google Scholar
Index Terms
- Using data mining techniques to address critical information exchange needs in disaster affected public-private networks
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