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

TL;DR

This paper investigates the relationship between event log complexity and process model complexity, revealing limited predictive power of existing measures and highlighting the need for better guidelines in process discovery.

Contribution

The study systematically analyzes multiple log and model complexity measures across various algorithms, providing insights into their relationships and limitations.

Findings

Only flower model complexity can be reliably predicted by log complexity.

Most log complexity measures do not effectively predict model complexity.

Current measures are insufficient for guiding the choice of process discovery algorithms.

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.