ProReco: A Process Discovery Recommender System
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910827807768576 |
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| author | Huang, Tsung-Hao Junied, Tarek Pegoraro, Marco van der Aalst, Wil M. P. |
| author_facet | Huang, Tsung-Hao Junied, Tarek Pegoraro, Marco van der Aalst, Wil M. P. |
| contents | Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last 25 years, there is no consensus on a dominating discovery algorithm. Selecting the most suitable discovery algorithm remains a challenge due to competing quality measures and diverse user requirements. Manually selecting the most suitable process discovery algorithm from a range of options for a given event log is a time-consuming and error-prone task. This paper introduces ProReco, a Process discovery Recommender system designed to recommend the most appropriate algorithm based on user preferences and event log characteristics. ProReco incorporates state-of-the-art discovery algorithms, extends the feature pools from previous work, and utilizes eXplainable AI (XAI) techniques to provide explanations for its recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ProReco: A Process Discovery Recommender System Huang, Tsung-Hao Junied, Tarek Pegoraro, Marco van der Aalst, Wil M. P. Machine Learning Information Retrieval Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last 25 years, there is no consensus on a dominating discovery algorithm. Selecting the most suitable discovery algorithm remains a challenge due to competing quality measures and diverse user requirements. Manually selecting the most suitable process discovery algorithm from a range of options for a given event log is a time-consuming and error-prone task. This paper introduces ProReco, a Process discovery Recommender system designed to recommend the most appropriate algorithm based on user preferences and event log characteristics. ProReco incorporates state-of-the-art discovery algorithms, extends the feature pools from previous work, and utilizes eXplainable AI (XAI) techniques to provide explanations for its recommendations. |
| title | ProReco: A Process Discovery Recommender System |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2502.10230 |