Data Mining Meets the Needs of Disaster Information Management

Techniques to efficiently discover, collect, organize, search, and disseminate real-time disaster information have become national priorities for efficient crisis management and disaster recovery tasks. We have developed techniques to facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management. We have designed and implemented two parallel systems: a web-based prototype of a Business Continuity Information Network system and an All-Hazard Disaster Situation Browser system that run on mobile devices. Data mining and information retrieval techniques help impacted communities better understand the current disaster situation and how the community is recovering. Specifically, information extraction integrates the input data from different sources; report summarization techniques generate brief reviews from a large collection of reports at different granularities; probabilistic models support dynamically generating query forms and information dashboard based on user feedback; and community generation and user recommendation techniques are adapted to help users identify potential contacts for report sharing and community organization. User studies with more than 200 participants from EOC personnel and companies demonstrate that our systems are very useful to gain insights about the disaster situation and for making decisions.

[1]  Ellen J. Bass,et al.  Emergency manager decision‐making and tornado warning communication , 2010 .

[2]  Magesh Jayapandian,et al.  Automating the Design and Construction of Query Forms , 2009, IEEE Transactions on Knowledge and Data Engineering.

[3]  Yiming Yang,et al.  Mining social networks for personalized email prioritization , 2009, KDD.

[4]  Magesh Jayapandian,et al.  Expressive query specification through form customization , 2008, EDBT '08.

[5]  Munmun De Choudhury,et al.  Inferring relevant social networks from interpersonal communication , 2010, WWW '10.

[6]  Liang Tang,et al.  Applying data mining techniques to address disaster information management challenges on mobile devices , 2011, KDD.

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

[8]  Christos Faloutsos,et al.  Fast Random Walk with Restart and Its Applications , 2006, Sixth International Conference on Data Mining (ICDM'06).

[9]  Tunga Güngör,et al.  Part-of-Speech Tagging , 2005 .

[10]  William W. Cohen,et al.  Ranking Users for Intelligent Message Addressing , 2008, ECIR.

[11]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

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

[13]  David A. McEntire,et al.  The Status of Emergency Management Theory: Issues, Barriers, and Recommendations for Improved Scholarship , 2004 .

[14]  Yi Deng,et al.  urvey of data management and analysis in disaster situations , 2010 .

[15]  Dragomir R. Radev,et al.  LexPageRank: Prestige in Multi-Document Text Summarization , 2004, EMNLP.

[16]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[17]  Magesh Jayapandian,et al.  Automated creation of a forms-based database query interface , 2008, Proc. VLDB Endow..

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

[19]  Liang Tang,et al.  Using data mining techniques to address critical information exchange needs in disaster affected public-private networks , 2010, KDD.

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

[21]  Jennifer Neville,et al.  Using Transactional Information to Predict Link Strength in Online Social Networks , 2009, ICWSM.

[22]  John D. Lafferty,et al.  A study of smoothing methods for language models applied to Ad Hoc information retrieval , 2001, SIGIR '01.

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

[24]  Michael Zink,et al.  Incorporating emergency management needs in the development of weather radar networks , 2009 .

[25]  Atro Voutilainen Part-of-Speech Tagging , 2005 .

[26]  Yossi Matias,et al.  Suggesting friends using the implicit social graph , 2010, KDD.

[27]  Koby Crammer,et al.  Learning to create data-integrating queries , 2008, Proc. VLDB Endow..

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

[29]  Liang Tang,et al.  Dynamic Query Forms for Database Queries , 2014, IEEE Transactions on Knowledge and Data Engineering.

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

[31]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[32]  Tao Li,et al.  An Empirical Study of Ontology-Based Multi-Document Summarization in Disaster Management , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[33]  Ellen J. Bass,et al.  Emergency Management Decision-Making during Severe Weather , 2006 .

[34]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..