Mind the Gap: A Formal Investigation of the Relationship Between Log and Model Complexity -- Extended Version

Abstract

Simple process models are key for effectively communicating the outcomes of process mining. An important question in this context is whether the complexity of event logs used as inputs to process discovery algorithms can serve as a reliable indicator of the complexity of the resulting process models. Although various complexity measures for both event logs and process models have been proposed in the literature, the relationship between input and output complexity remains largely unexplored. In particular, there are no established guidelines or theoretical foundations that explain how the complexity of an event log influences the complexity of the discovered model. This paper examines whether formal guarantees exist such that increasing the complexity of event logs leads to increased complexity in the discovered models. We study 18 log complexity measures and 17 process model complexity measures across five process discovery algorithms. Our findings reveal that only the complexity of the flower model can be established by an event log complexity measure. For all other algorithms, we investigate which log complexity measures influence the complexity of the discovered models. The results show that current log complexity measures are insufficient to decide which discovery algorithms to choose to construct simple models. We propose that authors of process discovery algorithms provide insights into which log complexity measures predict the complexity of their results.