The Imprecisions of Precision Measures in Process Mining
Databases
2018-05-07 v2 Artificial Intelligence
Logic in Computer Science
Software Engineering
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.
Cite
@article{arxiv.1705.03303,
title = {The Imprecisions of Precision Measures in Process Mining},
author = {Niek Tax and Xixi Lu and Natalia Sidorova and Dirk Fahland and Wil M. P. van der Aalst},
journal= {arXiv preprint arXiv:1705.03303},
year = {2018}
}