{"ID":2893829,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12504","arxiv_id":"2507.12504","title":"Transforming Football Data into Object-centric Event Logs with Spatial Context Information","abstract":"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","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2507.12504","url_pdf":"https://arxiv.org/pdf/2507.12504v1","authors":"[\"Vito Chan\",\"Lennart Ebert\",\"Paul-Julius Hillmann\",\"Christoffer Rubensson\",\"Stephan A. Fahrenkrog-Petersen\",\"Jan Mendling\"]","published":"2025-07-16T07:40:29Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.AI\"]","methods":"[]","has_code":false}