Title: Process Mining Embeddings: Learning Vector Representations for Petri Nets

Abstract: Process mining offers powerful techniques for discovering, analyzing, and enhancing real-world business processes. In this context, Petri nets provide an expressive means of modeling process behavior. However, directly analyzing and comparing intricate Petri net presents challenges. This study introduces PetriNet2Vec, a novel unsupervised methodology based on Natural Language Processing concepts inspired by Doc2Vec and designed to facilitate the effective comparison, clustering, and classification of process models represented as embedding vectors. These embedding vectors allow us to quantify similarities and relationships between different process models. Our methodology was experimentally validated using the PDC Dataset, featuring 96 diverse Petri net models. We performed cluster analysis, created UMAP visualizations, and trained a decision tree to provide compelling evidence for the capability of PetriNet2Vec to discern meaningful patterns and relationships among process models and their constituent tasks. Through a series of experiments, we demonstrated that PetriNet2Vec was capable of learning the structure of Petri nets, as well as the main properties used to simulate the process models of our dataset. Furthermore, our results showcase the utility of the learned embeddings in two crucial downstream tasks within process mining enhancement: process classification and process retrieval.