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Loïc Bidoux, Jean-Paul Pignon, & Frédérick Bénaben. (2014). A model driven system to support optimal collaborative processes design in crisis 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. 245–249). University Park, PA: The Pennsylvania State University.
Abstract: This paper presents a system dedicated to support crises managers that is focused on the collaboration issues of the actors involved in the response. Based on context knowledge, decision makers' objectives and responders' capabilities, the system designs in a semi-automatic way a set of collaborative process alternatives that can optimize coordination activities during an ongoing crisis resolution. The technical design of the system mixes optimization algorithms with inference of logical rules on an ontology. Candidate processes are evaluated through multi-criteria decision analysis and proposed to the decision-makers with associated key performance indicators to help them with their choice. The overall approach is model driven through a crisis meta-model and an axiomatic theory of crisis management.
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Olof Görnerup, Per Kreuger, & Daniel Gillblad. (2013). Autonomous accident monitoring using cellular network data. 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. 638–646). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions.
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Theo Dirk Meijler, & Frank Nietzold. (2011). Light-weight model-based realization of a B2B protocol and a SOA integration engine. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In emergency management, communication between the emergency management team and the outer world is essential. When using an emergency management system, such communication is often IT-based. To disburden the emergency management team, structured “B2B” messages may be used that correspond to the (foreseen) lifecycle of relevant entities in the emergency, such as threats and measures. The paper introduces an approach for the realization of a B2B messaging protocol and the corresponding integration engine, which maps message content to service calls, in the context of an emergency management system. The approach is light-weight and model-based, as protocols and integration engine are based on merely modeling the states and state transitions of objects in the system representing essential entities in the emergency. As the model is described in non-technical terms, this can be done by a non-IT expert.
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Massimiliano Tarquini, & Maurizio Morgano. (2013). Ethical challenges of participatory sensing for crisis information management. 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. 421–425). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: “Participatory Sensing is an approach to data collection and interpretation in which individuals, acting alone or in groups, use their personal mobile devices and web services to systematically explore interesting aspects of their worlds ranging from health to culture.”[ http://www.mobilizingcs.org/about/participatory-sensing] Data from the physical world of sensors and the virtual world of social networks and Linked Data can be combined into interesting high-level information. Sensor data can assist in localized information retrieval by giving the search engine direct access to events happening locally in the real world. Participatory sensing enables individuals and communities to collect and share granular, accurate data about a particular area. This paper describes work in progress within the FP7 EU-funded project SMART project to develop a multimedia search engine over content and information streaming from both the physical world and the Internet. We will identify some ethical problems regarding the use and storage of such data.
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