Automated Repair of Process Models with Non-Local Constraints Using State-Based Region Theory

TL;DR

This paper introduces a novel method to enhance process models by adding non-free-choice constructs using region-based techniques, improving model precision while preserving fitness, and demonstrating effectiveness on real datasets.

Contribution

It presents a new approach for repairing process models with non-local constraints using state-based region theory, combining process discovery and synthesis techniques.

Findings

Improves model precision with non-free-choice constructs

Preserves fitness with respect to event logs

Effective on synthetic and real datasets

Abstract

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.