Process Mining Embeddings: Learning Vector Representations for Petri Nets

TL;DR

This paper introduces PetriNet2Vec, an unsupervised embedding method inspired by Doc2Vec, that converts Petri nets into vectors to improve process model comparison, classification, and retrieval.

Contribution

The paper presents PetriNet2Vec, a novel unsupervised approach for embedding Petri nets into vector space, enabling better analysis and comparison of process models.

Findings

Embeddings effectively capture structural properties of Petri nets.

High accuracy in process classification tasks.

Efficient retrieval of similar process models using cosine similarity.

Abstract

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.