SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithms
This addresses the need for systematic analysis in process mining to improve algorithm evaluation and robustness, though it is incremental by focusing on associational effects rather than causal ones.
The paper tackles the problem of understanding how structural event log characteristics impact process mining algorithm metrics, by introducing SHAining to quantify their marginal contributions and analyzing over 22,000 event logs to identify which characteristics most affect metrics like fitness and precision.
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.