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Mining Local Process Models and Their Correlations

Abstract : Mining local patterns of process behavior is a vital tool for the analysis of event data that originates from flexible processes, which in general cannot be described by a single process model without overgeneralizing the allowed behavior. Several techniques for mining local patterns have been developed over the years, including Local Process Model (LPM) mining, episode mining, and the mining of frequent subtraces. These pattern mining techniques can be considered to be orthogonal, i.e., they provide different types of insights on the behavior observed in an event log. In this work, we demonstrate that the joint application of LPM mining and other patter mining techniques provides benefits over applying only one of them. First, we show how the output of a subtrace mining approach can be used to mine LPMs more efficiently. Secondly, we show how instances of LPMs can be correlated together to obtain larger LPMs, thus providing a more comprehensive overview of the overall process. We demonstrate both effects on a collection of real-life event logs.
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Laura Genga, Niek Tax, Nicola Zannone. Mining Local Process Models and Their Correlations. 7th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Dec 2017, Neuchatel, Switzerland. pp.65-88, ⟨10.1007/978-3-030-11638-5_4⟩. ⟨hal-02060700⟩



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