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.

Ordered data arises in many areas, e.g., in molec-ular dynamics and other spatial-temporal trajectories. While data points that are close in this order are related, common dimensionality reduction techniques cannot capture this relation or order. Thus, the information is lost in the low-dimensional representations. We introduce DROPP, which incorporates order into dimensionality reduction by adapting a Gaussian kernel function across the ordered covariances between data points. We find underlying principal components that are characteristic of the process that generated the data. In extensive experiments, we show DROPP's advantages over other dimensionality re-duction techniques on synthetic as well as real-world data sets from molecular dynamics and climate research: The principal components of different data sets that were generated by the same underlying mechanism are very similar to each other. They can, thus, be used for dimensionality reduction with low reconstruction errors along a set of data sets, allowing an explainable visual comparison of different data sets as well as good compression even for unseen data.