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Decomposing Petri nets for process mining: A generic approach

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Abstract

The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.

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Acknowledgements

This work was supported by the Basic Research Program of the National Research University Higher School of Economics (HSE) in Moscow.

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  1. Architecture of Information Systems, Eindhoven University of Technology, P.O. Box 513, 5600, MB, Eindhoven, The Netherlands

    Wil M. P. van der Aalst

  2. International Laboratory of Process-Aware Information Systems, National Research University Higher School of Economics (HSE), 33 Kirpichnaya Str., Moscow, Russia

    Wil M. P. van der Aalst

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Communicated by: Divyakant Agrawal.

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van der Aalst, W.M.P. Decomposing Petri nets for process mining: A generic approach. Distrib Parallel Databases 31, 471–507 (2013). https://doi.org/10.1007/s10619-013-7127-5

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