首次量化事件日志特征对流程挖掘算法性能的影响。
SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithms
- 提出SHAining方法，分析日志特征对算法指标的边际贡献。
- 基于2.2万条日志发现，某些特征显著影响精度与复杂度等指标。
- 揭示特征重要性与实际影响的相关性，适合评估算法鲁棒性。
流程挖掘旨在从事件日志中提取并分析洞察，但算法指标结果受日志结构特征影响极大。现有研究通常在固定的真实世界日志集上评估算法，缺乏对日志特征如何单独影响算法的系统分析。由于日志源自过程，特征常共现，本文关注关联效应而非因果效应，避免假设孤立因果关系这一常见疏漏。我们提出SHAining，首个量化不同日志特征对流程挖掘算法指标边际贡献的方法。以流程发现为下游任务，分析超过22,000条覆盖广泛特征的日志，揭示哪些特征对多种指标（如拟合度、精确度、复杂度）影响最大。同时，我们提供新见解：日志特征的价值与其实际影响相关，有助于评估算法稳健性。
原文摘要 · Abstract (English)
Process mining aims to extract and analyze insights from event logs, yet algorithm metric results vary widely depending on structural event log characteristics. Existing work often evaluates algorithms on a fixed set of real-world event logs but lacks a systematic analysis of how event log characteristics impact algorithms individually. Moreover, since event logs are generated from processes, where characteristics co-occur, we focus on associational rather than causal effects to assess how strong the overlapping individual characteristic affects evaluation metrics without assuming isolated causal effects, a factor often neglected by prior work. We introduce SHAining, the first approach to quantify the marginal contribution of varying event log characteristics to process mining algorithms' metrics. Using process discovery as a downstream task, we analyze over 22,000 event logs covering a wide span of characteristics to uncover which affect algorithms across metrics (e.g., fitness, precision, complexity) the most. Furthermore, we offer novel insights about how the value of event log characteristics correlates with their contributed impact, assessing the algorithm's robustness.
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