Automated Repair of Process Models with Non-Local Constraints Using State-Based Region Theory
This work addresses the issue of imprecise process models in business process management for users relying on automated discovery methods, representing an incremental improvement by combining existing techniques.
The paper tackles the problem of free-choice process models failing to capture indirect dependencies in event logs, proposing a novel approach that enhances these models with non-free-choice constructs using region-based techniques, resulting in preserved fitness and improved precision as demonstrated on synthetic and real-life datasets.
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.