Abstract-and-compare: A family of scalable precision measures for automated process discovery

Abstract

Automated process discovery techniques allow us to extract business process models from event logs. The quality of models discovered by these techniques can be assessed with respect to various criteria related to simplicity and accuracy. One of these criteria, namely precision, captures the extent to which the behavior allowed by a process model is observed in the log. While several measures of precision have been proposed, a recent study has shown that none of them fulfills a set of five axioms that capture intuitive properties behind the concept of precision. In addition, existing precision measures suffer from scalability issues when applied to models discovered from real-life event logs. This paper presents a family of precision measures based on the idea of comparing the k-th order Markovian abstraction of a process model against that of an event log. We demonstrate that this family of measures fulfils the aforementioned axioms for a suitably chosen value of k. We also empirically show that representative exemplars of this family of measures outperform a commonly used precision measure in terms of scalability and that they closely approximate two precision measures that have been proposed as possible ground truths.