George H. Bressler, Murray E. Jennex, & Eric G. Frost. (2012). X24 Mexico: Stronger together. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Can populations self-organize a crisis response? This is a work in progress report on Exercise 24, X24, Mexico, a follow up to the first two exercises, X24 and X24 Europe The X24 exercises used a variety of free and low-cost social media and web 2.0 tools to organize, plan, and manage local and international expertise and organizations in the response to a preset disaster scenario. The first X24 focused on Southern California, while the second X24, X24 Europe, focused on the Balkan area of Eastern Europe. These exercises involved over 12,500 participants for X24 while X24 Europe had over 49,000 participants. This paper presents an overview of the recently completed X24 Mexico exercise, as well as the preliminary results. © 2012 ISCRAM.
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José H. Canós-Cerdá, Juan Sánchez-Díaz, Vicent Orts, Carmen Penadés, Abel Gómez, & Marcos R. S. Borges. (2014). Turning emergency plans into executable artifacts. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 498–502). University Park, PA: The Pennsylvania State University.
Abstract: On the way to the improvement of Emergency Plans, we show how a structured specification of the response procedures allows transforming static plans into dynamic, executable entities that can drive the way different actors participate in crisis responses. Additionally, the execution of plans requires the definition of information access mechanisms allowing execution engines to provide an actor with all the information resources he or she needs to accomplish a response task. We describe work in progress to improve the SAGA's Plan definition Module and Plan Execution Engine to support information-rich plan execution.
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Stella Moehrle. (2012). Generic self-learning decision support system for large-scale disasters. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Large-scale disasters, particularly failures of critical infrastructures, are exceptional situations which cannot be solved with standard countermeasures. The crises are complex and the decision makers face acute time pressure to respond to the disaster. IT based decision support systems provide potential solutions and assist the decision making process. Many decision support systems in emergency response and management concentrate on one kind of disaster. Moreover, complex structures are modeled and recommendations are made rule-based. This work in progress paper describes the first steps towards the development of a generic and self-learning decision support system. The methodology used is case-based reasoning. The paper concludes with a sample emergency decision process. © 2012 ISCRAM.
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Nicholas Palmer, Roelof Kemp, Thilo Kielmann, & Henri Bal. (2012). RAVEN: Using smartphones for collaborative disaster data collection. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: In this paper we describe our work in progress on RAVEN, a framework, which makes it possible to build applications for collaborative editing of structured data on Android. RAVEN offers developers compile time tools, which use only the schema to generate all database handling components, edit and list user interfaces, as well as those needed for data synchronization, significantly reducing development effort. In addition, RAVEN also offers the ability to do the same work, entirely at runtime, using only a smartphone. With RAVEN it is possible to construct data oriented applications on phone at any time, including during a disaster. Users can share their applications simply by sharing the database and corresponding schema. Thus, RAVEN enables completely decentralized application creation, sharing, and data distribution, avoiding issues of connectivity to centralized resources. In this paper we show that with RAVEN it is possible to construct a new application at runtime and compare the results with an equivalent custom-built application. © 2012 ISCRAM.
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Jens Pottebaum, Robin Marterer, & Steffen Schneider. (2014). Taxonomy of IT support for training emergency response & management. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 374–378). University Park, PA: The Pennsylvania State University.
Abstract: Training is a prerequisite for effective and efficient emergency response and management. Information technology (IT) offers high potential to support various educational methods and environments. One example for interdependent use cases is given by planning, controlling, observation and debriefing of training exercises. Dedicated IT systems are available to support these use cases; nevertheless, there is no joint understanding of system use cases, types and functionality as a fundament for scientific and technological progress. This work in progress paper presents results of literature and market research complemented by expert interviews leading to a taxonomy of relevant IT components and systems.
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Christopher W. Zobel. (2013). Analytically comparing disaster recovery following the 2012 derecho. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 678–682). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: This work in progress paper discusses analytically characterizing nonlinear recovery behavior through the context of the derecho windstorm that struck the mid-Atlantic United States in the summer of 2012. The focus is on the recovery efforts of the Appalachian Power Company, and the discussion includes a look at the need for communicating the progress of such recovery efforts to the public. Publicly available recovery data is analyzed and compared with respect to the relative behaviors exhibited by two different nonlinear recovery processes, and some of the implications for understanding the efficiency of different disaster recovery operations are discussed.
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