Summary of Process Mining Embeddings: Learning Vector Representations For Petri Nets, by Juan G. Colonna et al.
Process Mining Embeddings: Learning Vector Representations for Petri Nets
by Juan G. Colonna, Ahmed A. Fares, Márcio Duarte, Ricardo Sousa
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel unsupervised methodology called PetriNet2Vec is introduced to facilitate effective comparison and analysis of complex Petri nets in Process Mining. Inspired by Doc2Vec, this approach converts Petri nets into embedding vectors, enabling the comparison, clustering, and classification of process models. The method is validated using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec accurately captures the structural properties of process models, allowing for accurate process classification and efficient process retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PetriNet2Vec is a new way to understand and compare business processes. It takes complex Petri nets (like diagrams) and turns them into special numbers called embeddings. This helps us group similar processes together or find the right one quickly. The team tested it on 96 different Petri net models and found that it worked well. They used it for two important tasks: figuring out what kind of process a model is, and finding similar processes. |
Keywords
» Artificial intelligence » Classification » Clustering » Doc2vec » Embedding » Unsupervised
