A behavioral similarity measure between labeled Petri nets based on principal transition sequences

Being able to determine the degree of similarity between process models is important for management, reuse, and analysis of business process models. In this paper we propose a novel method to determine the degree of similarity between process models, which exploits their semantics. Our approach is designed for labeled Petri nets as these can be seen as a foundational theory for process modeling. As the set of traces of a labeled Petri net may be infinite, the challenge is to find a way to represent behavioral characteristics of a net in a finite manner. Therefore, the proposed similarity measure is based on the notion of so-called "principal transition sequences", which aim to provide an approximation of the essence of a process model. This paper defines a novel similarity measure, proposes a method to compute it, and demonstrates that it offers certain benefits with respect to the state-of-the-art in this field