A Behavioral Similarity Measure between Labeled Petri Nets Based on Principal Transition Sequences

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Abstract

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.