Framework for Grouping Local Process Models

• URL: http://arxiv.org/abs/2607.04856v1
• Date: Mon, 06 Jul 2026 09:32:07 GMT
• Title: Framework for Grouping Local Process Models
• Abstract summary: Local Process Models describe patterns in event data considering sequence, choice, and loop.<n>Current sentiment is that the top-scoring LPMs form the optimal sample to be presented.<n>We propose a framework for grouping LPMs and creating an optimal sample by taking one representative LPM for each group.
• Score: 1.4610038284393168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent years, process mining has proved successful in the analysis and improvement of operational processes. More often than not, surprising findings are found when one does not consider the full process, making LPMs and their discovery highly valuable. However, similar to other pattern mining approaches, LPM discovery algorithms face the problems of model explosion and model repetition, i.e., the algorithms may create hundreds if not thousands of LPMs, and subsets of them are close in structure or behavior. Practically, no analyst would be able to comb through thousands of LPMs leading to using a sample of LPMs that are easily accessible. The current sentiment is that the top-scoring LPMs form the optimal sample to be presented. However, different applications should demand a different optimal sample. With this work, we show that if the goal of the mined LPMs is to understand a process, using the top-scoring LPMs as an optimal sample is a poor choice because of high repetition. We propose a framework for grouping LPMs and creating an optimal sample by taking one representative LPM for each group. We measure similarity between models via established process model similarity measures or by comparing the context in which an LPM appears. The context is formed using data attributes available in the underlying event logs. We demonstrate the usefulness of grouping on multiple event logs by comparing repetition and coverage between samples comprised of the top-scoring models and the representatives of discovered groups.

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