Model repair supported by frequent anomalous local instance graphs

Abstract

Model repair techniques aim at automatically updating a process model to incorporate behaviors that are observed in reality but are not compliant with the original model. Most state-of-the-art techniques focus on the fitness of the repaired models, with the goal of including single anomalous behaviors observed in a log in the form of the events. This often hampers the precision of the obtained models, which end up allowing much more behaviors than intended. In the quest of techniques avoiding this over-generalization pitfall, some notion of higher-level anomalous structure is taken into account. The type of structure considered is however typically limited to sequences of low-level events. In this work, we introduce a novel repair approach targeting more general high-level anomalous structures. To do this, we exploit instance graph representations of anomalous behaviors, that can be derived from the event log and the original process model. Our experiments show that considering high-level anomalies allows to generate repaired models that incorporate the behaviors of interest while maintaining precision and simplicity closer to the original model.