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Beyond Log and Model Moves in Conformance Checking: Discovering Process-Level Deviation Patterns

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Business Process Management (BPM 2024)

Abstract

Process managers apply conformance checking techniques to identify deviations between the desired and the actual execution of a process. From a process-level perspective, these deviations often involve multiple interrelated events, for example if activities are executed in the wrong order or are unnecessarily repeated. However, state-of-the-art conformance checking techniques do not reveal these process-level deviations, instead identifying only event-level deviations in the form of inserted or skipped events. To address this shortcoming, this paper presents an approach that discovers process-level deviations from event-level insights provided by alignment-based conformance checking techniques. These deviations are discovered as instantiations of five commonly used patterns of non-conformance: inserted, skipped, repeated, replaced, and swapped. The approach is designed to choose patterns according to a user’s preferences and contextualize them within parallelism and choices in the process model. Our evaluation shows that it reliably detects process-level deviations, thus providing process managers with more comprehensive information on process conformance.

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Notes

  1. 1.

    Note that in this paper, any alignment visualization places the trace above the model sequence.

  2. 2.

    Note that the original source [15] also proposes a loop pattern, but in the context of alignments, this is simply a specific version of the repeated pattern.

  3. 3.

    https://dx.doi.org/10.6084/m9.figshare.25942474.

  4. 4.

    https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F.

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Correspondence to Michael Grohs .

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Grohs, M., van der Aa, H., Rehse, JR. (2024). Beyond Log and Model Moves in Conformance Checking: Discovering Process-Level Deviation Patterns. In: Marrella, A., Resinas, M., Jans, M., Rosemann, M. (eds) Business Process Management. BPM 2024. Lecture Notes in Computer Science, vol 14940. Springer, Cham. https://doi.org/10.1007/978-3-031-70396-6_22

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  • DOI: https://doi.org/10.1007/978-3-031-70396-6_22

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