Abstract
Process mining provides a set of techniques and algorithms to analyze, support, and improve business processes based on process execution data. Process discovery aims at deducing a representative process model of real-world execution. So far, process discovery algorithms have been mainly compared regarding their output quality but not yet with regard to their functional capabilities. The well-established workflow control flow patterns imperatively describe process behavior, originally used to compare modeling languages, but to date, not to compare discovery algorithms. In this work, we analyze a representative set of process discovery algorithms with regard to their coverage of 23 control flow patterns. For this purpose, we implemented each workflow pattern as an executable colored Petri net, simulated it, and ran various discovery algorithms on the obtained event log. A comparison of the results shows that the discovery algorithms mainly cover basic control flow patterns and iterative structures, while multi-instance, state-base, and cancellation patterns are only partially covered.
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Notes
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Note the distinction here between process events and log events.
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Andree, K., Hoang, M., Dannenberg, F., Weber, I., Pufahl, L. (2024). Discovery of Workflow Patterns - A Comparison of Process Discovery Algorithms. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_14
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