# An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining

arXiv: https://arxiv.org/abs/2007.09310 · HTML: https://mlsanity.com/paper/2007.09310/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes](https://mlsanity.com/paper/2403.09232/) (2024)
- [Stochastic Directly-Follows Process Discovery Using Grammatical Inference](https://mlsanity.com/paper/2312.05433/) (2023)
- [Process Model Forecasting Using Time Series Analysis of Event Sequence Data](https://mlsanity.com/paper/2105.01092/) (2021)
- [Bootstrapping Generalization of Process Models Discovered From Event Data](https://mlsanity.com/paper/2107.03876/) (2021)
- [DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining](https://mlsanity.com/paper/2606.14283/) (2026)
- [Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring](https://mlsanity.com/paper/2607.17783/) (2026)

## Abstract (arXiv)

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 - and seek to incorporate this knowledge in their analyses. Here we present an entropic relevance measure for stochastic conformance checking, computed as the average number of bits required to compress each of the log's traces, based on the structure and information about relative likelihoods provided by the model. The measure penalizes traces from the event log not captured by the model and traces described by the model but absent in the event log, thus addressing both precision and recall quality criteria at the same time. We further show that entropic relevance is computable in time linear in the size of the log, and provide evaluation outcomes that demonstrate the feasibility of using the new approach in industrial settings.