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Anne-Marie Barthe-Delanoë, & Wenxin Mu. (2020). Towards a Context-Aware Systemic Risk Management Framework for the Crisis Response. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 1122–1129). Blacksburg, VA (USA): Virginia Tech.
Abstract: Crisis response is, as any other collaborative networked organization, challenged by changes and vulnerabilities. Moreover, as a complex system with distributed activities and numerous interdependencies, considering the risk of such an organization at a systemic level, including time and space dimensions, is necessary. Systemic risk management is a topic traditionally studied in the finance area. Even if a few researches now focus on the supply chain management area (a more relatable domain regarding crisis response), there is even fewer literature regarding systemic risk management for the crisis response. Thus, this paper proposes first to define systemic risk related to the case of the crisis response. Then, a framework for context-aware systemic risk management is presented, to support the design as well as the follow-up of the crisis response, meeting one of the challenges of the Sendai Framework for Disaster Risk Reduction.
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Jiayao Li, Juanqiong Gou, Wenxin Mu, & Liyu Peng. (2017). Modeling of Railway Risk Inter-Relation based on the study of Accident Context. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 328–340). Albi, France: Iscram.
Abstract: In order to detect and control the critical potential risk source of railway more scientifically, more reasonably and more accurately in complex accident context, a knowledge modeling method of risk inter-relation is proposed based on ontology modeling of accident context. First, the mechanism of accident causation is summarized based on the accident case analysis. Then, the knowledge model of accident cause is built based on ontology theory, including the ontology model of two context instances. Last but not least, the risk inter-relation rules with different dimensions of inter-relation patterns are inferred based on the instantiation of ontology model. The two context instances are used to illustrate the identification process of risk inter-relation. The results prove the rationality of the method, which can provide a reference for the precise railway risk prevention.
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