Transforming Football Data into Object-centric Event Logs with Spatial Context Information
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915394954985472 |
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| author | Chan, Vito Ebert, Lennart Hillmann, Paul-Julius Rubensson, Christoffer Fahrenkrog-Petersen, Stephan A. Mendling, Jan |
| author_facet | Chan, Vito Ebert, Lennart Hillmann, Paul-Julius Rubensson, Christoffer Fahrenkrog-Petersen, Stephan A. Mendling, Jan |
| contents | Object-centric event logs expand the conventional single-case notion event log by considering multiple objects, allowing for the analysis of more complex and realistic process behavior. However, the number of real-world object-centric event logs remains limited, and further studies are needed to test their usefulness. The increasing availability of data from team sports can facilitate object-centric process mining, leveraging both real-world data and suitable use cases. In this paper, we present a framework for transforming football (soccer) data into an object-centric event log, further enhanced with a spatial dimension. We demonstrate the effectiveness of our framework by generating object-centric event logs based on real-world football data and discuss the results for varying process representations. With our paper, we provide the first example for object-centric event logs in football analytics. Future work should consider variant analysis and filtering techniques to better handle variability |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transforming Football Data into Object-centric Event Logs with Spatial Context Information Chan, Vito Ebert, Lennart Hillmann, Paul-Julius Rubensson, Christoffer Fahrenkrog-Petersen, Stephan A. Mendling, Jan Databases Artificial Intelligence Object-centric event logs expand the conventional single-case notion event log by considering multiple objects, allowing for the analysis of more complex and realistic process behavior. However, the number of real-world object-centric event logs remains limited, and further studies are needed to test their usefulness. The increasing availability of data from team sports can facilitate object-centric process mining, leveraging both real-world data and suitable use cases. In this paper, we present a framework for transforming football (soccer) data into an object-centric event log, further enhanced with a spatial dimension. We demonstrate the effectiveness of our framework by generating object-centric event logs based on real-world football data and discuss the results for varying process representations. With our paper, we provide the first example for object-centric event logs in football analytics. Future work should consider variant analysis and filtering techniques to better handle variability |
| title | Transforming Football Data into Object-centric Event Logs with Spatial Context Information |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2507.12504 |