An entropic relevance measure for stochastic conformance checking in process mining

Abstract

Given an event log as a collection of recorded real-world process traces, process mining aims to automatically construct a process model that is both simple and provides a useful explanation of the traces. Conformance checking techniques are then employed to characterize and quantify commonalities and discrepancies between the log's traces and the candidate models. Recent approaches to conformance checking acknowledge that the elements being compared are inherently stochastic-for example, some traces occur frequently and others infrequently- A nd seek to incorporate this knowledge in their analyses.Here we present an entropic relevance measure for stochastic conformance checking, computed as..