# The Imprecisions of Precision Measures in Process Mining

## TL;DR

This paper critically examines existing precision measures in process mining, demonstrating that none reliably quantify over-approximation across diverse models and logs, highlighting the need for more consistent evaluation methods.

## Contribution

It introduces axioms for consistent precision measurement and shows that current measures fail to meet these criteria through counter-examples.

## Key findings

- Existing measures do not consistently quantify precision.
- None of the current measures satisfy the proposed axioms.
- Highlights the need for improved precision metrics in process mining.

## Abstract

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.