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.

通过使用嵌入向量表示的 PetriNet2Vec 方法，我们展示了在过程挖掘增强中，PetriNet2Vec 从 Petri 网模型中学习结构和特性，以及在过程分类和过程检索中所展现的实用性。