Using Domain Knowledge to Enhance Process Mining Results

Abstract : Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining.
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Communication dans un congrès
Paolo Ceravolo; Stefanie Rinderle-Ma. 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Dec 2015, Vienna, Austria. Springer International Publishing, Lecture Notes in Business Information Processing, LNBIP-244, pp.76-104, 2017, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-319-53435-0_4〉
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P. Dixit, J. Buijs, Wil Aalst, B. Hompes, J. Buurman. Using Domain Knowledge to Enhance Process Mining Results. Paolo Ceravolo; Stefanie Rinderle-Ma. 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Dec 2015, Vienna, Austria. Springer International Publishing, Lecture Notes in Business Information Processing, LNBIP-244, pp.76-104, 2017, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-319-53435-0_4〉. 〈hal-01651892〉

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