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.
Abstract (translated)
过程挖掘提供了发现、分析和增强现实商业流程的强大技术。在这个背景下，Petri网提供了一种富有表现力的建模过程行为的手段。然而，直接分析和比较复杂的Petri网存在挑战。本研究介绍了一种新颖的无监督方法——PetriNet2Vec，基于自然语言处理概念，受到Doc2Vec的启发，并旨在促进对用嵌入向量表示的过程模型的有效比较、聚类和分类。这些嵌入向量使我们能够量化不同过程模型的相似性和关系。我们的研究通过使用PDC数据集，其中包括96个具有多样性的Petri网模型，通过实验验证了PetriNet2Vec的建模能力。我们进行了聚类分析，创建了UMAP可视化，并训练了一个决策树，以提供有关PetriNet2Vec在过程模型及其组成部分任务之间辨别有意义的模式和关系的令人信服的证据。通过一系列实验，我们证明了PetriNet2Vec能够学习Petri网的结构，以及我们数据集中的过程模型的主要性质。此外，我们的结果展示了学习嵌入向量在过程挖掘增强中的两个关键下游任务中的实用性：过程分类和过程检索。
URL
https://arxiv.org/abs/2404.17129
