Aaron Burgman, Nikhil Kalghatgi, Erika Darling, Chris M. Newbern, Kristine Recktenwald, Shawn Chin, et al. (2006). Emergency data analysis via semantic lensing. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 334–338). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Emergency situations often play out over extended geographic regions and can present response personnel with numerous types of data at various level of detail. Such data may be displayed in mapping software tools that organize the data into layers. Sufficiently complex scenarios can result in dense, occluded, and cluttered map displays. We investigated a localized, detail-on-demand filtering strategy called semantic lensing that in certain situations provides a more efficient and desirable approach than filtering global layers for mitigating clutter and occlusion. An initial formal user study with these semantic lenses has shown their value in aiding decision makers during tasks that might occur during detection of and response to emergency situations. Completion times are significantly faster when using lenses, and workloads are significantly lower. Future work will evaluate additional features and task-specific applicability, and may support the distribution of such a lens tool to emergency preparedness and response personnel.
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Maurizio Marchese, Lorenzino Vaccari, Pavel Shvaiko, & Juan Pane. (2008). An application of approximate ontology matching in eResponse. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 294–304). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Ontology matching is a key problem in many metadata intensive application domains, including emergency response, data integration, peer-to-peer information sharing, web service composition, and query answering on the web. In this paper we present an emergency response scenario based on the organizational model as used in Trentino region, Italy. We provide a formalization of this scenario with the help of lightweight coordination calculus. Then, we discuss an automatic approximate structure preserving matching algorithm which we applied within the emergency response scenario. The evaluation results, though preliminary, are encouraging.
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Fahem Kebair, & Frédéric Serin. (2008). Towards an intelligent system for risk prevention and emergency management. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 526–535). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Making a decision in a changeable and dynamic environment is an arduous task owing to the lack of information, their uncertainties and the unawareness of planners about the future evolution of incidents. The use of a decision support system is an efficient solution for this issue. Such a system can help emergency planners and responders to detect possible emergencies, as well as to suggest and evaluate possible courses of action to deal with the emergency. We are interested in our work to the modelling of a monitoring preventive and emergency management system, wherein we stress the generic aspect. In this paper we propose an agent-based architecture of this system and we describe a first step of our approach which is the modeling of information and their representation using a multiagent system.
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Paola Di Maio. (2008). Ontologies for networked centric emergency mangement operations. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 177–188). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Emergency Management, like other fields of Operations, consists of information, communication and decision making. Thanks to the pervasiveness of real time networked infrastructures, such as the internet and the web, new models of operations are emerging, designed to leverage the aggregate the power of 'collective intelligence' and 'distributed action' facilitated by 'open world' systems environments. In order to develop effective information systems capable of supporting the distributed nature of emerging 'architectures of participation', it is necessary to devise adequate 'semantic structures', which in turn rely on sound and explicit conceptual frameworks, such as ontologies. However, there aren't enough 'ontologies' in the public domain that can be referenced to establish compatibility of architectures and serve as guidelines for the development of open, neutral and accountable information systems. In this paper we a) describe and analyse the 'distributed' and 'networked' nature of emergency operations b) put forward the notion information systems to support of emergency management today should be modeled on 'distributed' and networked organizational structures, and that ontologies in this domain should be built accordingly.
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André Dittrich, & Christian Lucas. (2013). A step towards real-time analysis of major disaster events based on tweets. 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. 868–874). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The most popular micro blogging platform Twitter has been the topic of a variety of research papers related to disaster and crisis management. As an essential first step and basis for a real-time methodology to exploit Twitter for event detection, localization and ultimately semantic content analysis, a functional model to describe the amount of tweets during a day has been developed. It was derived from a corpus of messages in an exemplary area of investigation. To satisfy the different daily behavior on particular days, two types of days are distinguished in this paper. Moreover, keyword-adjusted data is used to point out the potential of semantic tweet analysis in following steps. The consideration of spatial event descriptions in relevant tweets could significantly improve and accelerate the perception of a disaster. The results from the conducted tests demonstrate the capability of the functional model to detect events with significant social impact in Twitter data.
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