Extraction of Object-Centric Event Logs through Virtual Knowledge Graphs (Extended Abstract)

Jing Xiong, Guohui Xiao, Tahir Emre Kalayci, Marco Montali, Zhenzhen Gu, and Diego Calvanese

Proc. of the 35th Int. Workshop on Description Logics (DL 2022). CEUR Workshop Proceedings, http://ceur-ws.org/. 2022. To appear.

Process mining is a family of techniques that supports the analysis of operational processes based on event logs. Among the existing event log formats, the IEEE standard eXtensible Event Stream (XES) is the most widely adopted. In XES, each event must be related to a single case object, which may lead to convergence and divergence problems. To solve such issues, object-centric approaches become promising, where objects are the central notion, and one event may refer to multiple objects. In particular, the Object-Centric Event Logs (OCEL) standard has been proposed recently. However, the crucial problem of extracting OCEL logs from external sources is still largely unexplored. In this paper, we try to fill this gap by leveraging the Virtual Knowledge Graph (VKG) approach to access data in relational databases. We have implemented this approach in the OnProm system, extending it from XES to OCEL support.


@inproceedings{DL-2022-onprom,
  title = "Extraction of Object-Centric Event Logs through Virtual Knowledge
Graphs (Extended Abstract)",
   year = "2022",
   author = "Jing Xiong and Guohui Xiao and Kalayci, Tahir Emre and Marco
Montali and Zhenzhen Gu and Diego Calvanese",
   booktitle = "Proc. of the 35th Int. Workshop on Description Logics
(DL 2022)",
   publisher = "CEUR-WS.org",
   series = "CEUR Workshop Proceedings, http://ceur-ws.org/",
   note = "To appear",
}
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