Applying data mining techniques to address disaster information management challenges on mobile devices

Authors:
Li Zheng;Chao Shen;Liang Tang;Tao Li;Steve Luis;Shu-Ching Chen
Affiliations:
Florida International University, Miami, FL, USA;Florida International University, Miami, FL, USA;Florida International University, Miami, FL, USA;Florida International University, Miami, FL, USA;Florida International University, Miami, FL, USA;Florida International University, Miami, FL, USA
Venue:
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2011

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Abstract

The improvement of Crisis Management and Disaster Recovery techniques are national priorities in the wake of man-made and nature inflicted calamities of the last decade. Our prior work has demonstrated that the efficiency of sharing and managing information plays an important role in business recovery efforts after disaster event. With the proliferation of smart phones and wireless tablets, professionals who have an operational responsibility in disaster situations are relying on such devices to maintain communication. Further, with the rise of social media, technology savvy consumers are also using these devices extensively for situational updates. In this paper, we address several critical tasks which can facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management. We design and implement an All-Hazard Disaster Situation Browser (ADSB) system that runs on Apple's mobile operating system (iOS) and iPhone and iPad mobile devices. Our proposed techniques create a collaborative solution on a mobile platform using advanced data mining and information retrieval techniques for disaster preparedness and recovery that helps impacted communities better understand the current disaster situation and how the community is recovering. Specifically, hierarchical summarization techniques are used to generate brief reviews from a large collection of reports at different granularities; probabilistic models are proposed to dynamically generate query forms based on user's feedback; and recommendation techniques are adapted to help users identify potential contacts for report sharing and community organization. Furthermore, the developed techniques are designed to be all-hazard capable so that they can be used in earthquake, terrorism, or other unanticipated disaster situations.