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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15411 |
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| _version_ | 1866913950475485184 |
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| author | Gherissi, Wissam Acheli, Mehdi Haddad, Joyce El Grigori, Daniela |
| author_facet | Gherissi, Wissam Acheli, Mehdi Haddad, Joyce El Grigori, Daniela |
| contents | Object-centric predictive process monitoring explores and utilizes object-centric event logs to enhance process predictions. The main challenge lies in extracting relevant information and building effective models. In this paper, we propose an end-to-end model that predicts future process behavior, focusing on two tasks: next activity prediction and next event time. The proposed model employs a graph attention network to encode activities and their relationships, combined with an LSTM network to handle temporal dependencies. Evaluated on one reallife and three synthetic event logs, the model demonstrates competitive performance compared to state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15411 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictive Process Monitoring Using Object-centric Graph Embeddings Gherissi, Wissam Acheli, Mehdi Haddad, Joyce El Grigori, Daniela Artificial Intelligence Machine Learning Object-centric predictive process monitoring explores and utilizes object-centric event logs to enhance process predictions. The main challenge lies in extracting relevant information and building effective models. In this paper, we propose an end-to-end model that predicts future process behavior, focusing on two tasks: next activity prediction and next event time. The proposed model employs a graph attention network to encode activities and their relationships, combined with an LSTM network to handle temporal dependencies. Evaluated on one reallife and three synthetic event logs, the model demonstrates competitive performance compared to state-of-the-art methods. |
| title | Predictive Process Monitoring Using Object-centric Graph Embeddings |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.15411 |