Automated Repair of Process Models Using Non-Local Constraints

Abstract

State-of-the-art process discovery methods construct free-choice process models from event logs. Hence, the constructed models do not take into account indirect dependencies between events. Whenever the input behavior is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for the enhancement of free-choice process models, by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from both 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 process models discovered from event logs.