Using Free-Choice Nets for Process Mining and Business Process Management

Free-choice nets, a subclass of Petri nets, have been studied for decades. They are interesting because they have many desirable properties normal Petri nets do not have and can be analyzed efficiently. Although the majority of process models used in practice are inherently free-choice, most users (even modeling experts) are not aware of free-choice net theory and associated analysis techniques. This paper discusses free-choice nets in the context of process mining and business process management. For example, state-of-the-art process discovery algorithms like the inductive miner produce process models that are free-choice. Also, hand-made process models using languages like BPMN tend to be free-choice because choice and synchronization are separated in different modeling elements. Therefore, we introduce basic notions and results for this important class of process models. Moreover, we also present new results for free-choice nets particularly relevant for process mining. For example, we elaborate on home clusters and lucency as closely-related and desirable correctness notions. We also discuss the limitations of free-choice nets in process mining and business process management, and suggest research directions to extend free-choice nets with non-local dependencies.