Measuring Fitness and Precision of Automatically Discovered Process Models: A Principled and Scalable Approach

Abstract

Automated process discovery techniques allow us to generate a process model from an event log consisting of a collection of business process execution traces. The quality of process models generated by these techniques can be assessed with respect to several criteria, including fitness, which captures the degree to which the generated process model is able to recognize the traces in the event log, and precision, which captures the extent to which the behavior allowed by the process model is observed in the event log. A range of fitness and precision measures have been proposed in the literature. However, existing measures in this field do not fulfil basic monotonicity properties and/or they ..