# Bootstrapping Generalization of Process Models Discovered From Event Data

arXiv: https://arxiv.org/abs/2107.03876 · HTML: https://mlsanity.com/paper/2107.03876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Adversarial System Variant Approximation to Quantify Process Model Generalization](https://mlsanity.com/paper/2003.12168/) (2020)
- [An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining](https://mlsanity.com/paper/2007.09310/) (2020)
- [Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)](https://mlsanity.com/paper/1909.02393/) (2019)
- [All That Glitters Is Not Gold: Towards Process Discovery Techniques with Guarantees](https://mlsanity.com/paper/2012.12764/) (2020)
- [Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining](https://mlsanity.com/paper/2008.09558/) (2020)

## Built on by (most cited first, 1 of 1)

- [Masking Neural Networks Using Reachability Graphs to Predict Process Events](https://mlsanity.com/paper/2108.00404/) (2021)

## Abstract (arXiv)

Process mining extracts value from the traces recorded in the event logs of IT-systems, with process discovery the task of inferring a process model for a log emitted by some unknown system. Generalization is one of the quality criteria applied to process models to quantify how well the model describes future executions of the system. Generalization is also perhaps the least understood of those criteria, with that lack primarily a consequence of it measuring properties over the entire future behavior of the system when the only available sample of behavior is that provided by the log. In this paper, we apply a bootstrap approach from computational statistics, allowing us to define an estimator of the model's generalization based on the log it was discovered from. We show that standard process mining assumptions lead to a consistent estimator that makes fewer errors as the quality of the log increases. Experiments confirm the ability of the approach to support industry-scale data-driven systems engineering.