Using data mining techniques to address critical information exchange needs in disaster affected public-private networks

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.

[1]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[3]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[4]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[5]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[6]  Yi Deng,et al.  Evolutionary document summarization for disaster management , 2009, SIGIR.

[7]  Fernando Pereira,et al.  Shallow Parsing with Conditional Random Fields , 2003, NAACL.

[8]  Ronald E. Anderson.  Social Impacts of Computing: Codes of Professional Ethics , 1992 .

[9]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[10]  Marilyn Schwartz,et al.  Guidelines for bias-free writing , 1995 .

[11]  Chi-Hoon Lee,et al.  On Data Clustering Analysis: Scalability, Constraints, and Validation , 2002, PAKDD.

[12]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[13]  Wendy E. Mackay,et al.  Ethics, lies and videotape… , 1995, CHI '95.

[14]  Chi-Hoon. Lee.  Density-based clustering of spatial data in the presence of physical constraints , 2002 .

[15]  Yi Deng,et al.  Towards a business continuity information network for rapid disaster recovery , 2008, DG.O.