The Imprecisions of Precision Measures in Process Mining
This work identifies a foundational inconsistency in process mining metrics, which is crucial for researchers and practitioners relying on these measures for model evaluation.
The paper addresses the lack of validation for precision measures in process mining, which quantify over-approximation in process models, and demonstrates through counter-examples that no existing measure consistently achieves this aim.
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all models and logs. This paper fills this gap by postulating a number of axioms for quantifying precision consistently for any log and any model. Further, we show through counter-examples that none of the existing measures consistently quantifies precision.