Bootstrapping Generalization of Process Models Discovered From Event Data

TL;DR

This paper introduces a bootstrap-based method to estimate the generalization of process models from event logs, improving understanding of model quality for future system behavior prediction.

Contribution

It proposes a novel bootstrap approach to quantify process model generalization, addressing a key challenge in process mining with theoretical and practical validation.

Findings

Estimator makes fewer errors with higher log quality

Approach supports industry-scale data-driven systems engineering

Consistent estimator under standard process mining assumptions

Abstract

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.