Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining

TL;DR

Entropia introduces entropy-based conformance measures for process mining, enabling automatic, efficient quantification of model precision and recall against event logs, with a focus on non-deterministic and stochastic aspects.

Contribution

This paper presents Entropia, a novel command-line tool implementing entropy-based measures for conformance checking in process mining, enhancing automatic and efficient quality assessment.

Findings

Measures can be computed quickly.

Measures effectively quantify precision and recall.

Properties of measures support non-deterministic and stochastic models.

Abstract

This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly.