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

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.

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

[2]  Roy Fielding,et al.  Architectural Styles and the Design of Network-based Software Architectures"; Doctoral dissertation , 2000 .

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

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

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

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

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

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

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

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

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

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

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

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

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