Ariouat, HananeAndonoff, EricHanachi, Chihab2019-01-032019-01-032019-01-08978-0-9981331-2-6http://hdl.handle.net/10125/59737Crisis resolution is often based on official government plans that provide guidelines. In real time, when a crisis occurs, one or several plans have to be chosen, merged, refined to meet the specific requirements of the crisis, and then launched. Plans are often in a textual format, which makes their interpretation ambiguous and error prone. Therefore, in real time, the coordination of stakeholders becomes difficult and time consuming. Given these drawbacks, the transformation of a plan into a process provides several advantages: i) an accurate and machine-readable specification of coordination of actions to be done in the field, ii) a better common understanding between stakeholders responsible for these actions and iii) a mean to analyze, simulate and evaluate the crisis response before launching it. The problem being addressed in this paper is “how to deduce a process for driving crisis resolution from business knowledge (plans, stakeholders and their capacities) and relevant facts observed in the impacted field”. This paper presents first a meta-model for capturing business knowledge and crisis situation and then a deduction approach deriving a process in a BPMN-like format. Flood of the Loire in June 2016 serves as a support for approach experiment.10 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalDisaster Information, Technology, and Resilience in Digital GovernmentDigital GovernmentCrisis Management, Flood , Process Deduction, Process MiningConference Paper10.24251/HICSS.2019.364