Detecting Changes in Process Behavior Using Comparative Case Clustering

Abstract : Real-life business processes are complex and often exhibit a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary, flexible business processes. This paper presents a novel comparative case clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation using both synthetic and real-life event data shows our technique can provide these insights.
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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.54-75, 2017, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-319-53435-0_3〉
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B. Hompes, J. Buijs, Wil Aalst, P. Dixit, J. Buurman. Detecting Changes in Process Behavior Using Comparative Case Clustering. 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.54-75, 2017, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-319-53435-0_3〉. 〈hal-01651887〉

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