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Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

Segmentation and Process Assignment of Semi-Structured Event Logs

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DOI: http://dx.doi.org/10.15439/2025F4755

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 231–241 ()

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Abstract. Process mining provides valuable insights by discovering process models from execution logs. However, its effectiveness depends heavily on high-quality, well-structured logs. Many real-world systems produce low-level, semi-structured logs lacking clear process identifiers, causing misalignment with their intended process models. This paper introduces a method for structuring raw event logs by segmenting event streams and mapping them to known processes. Using process traces from experienced users, we develop a model that infers process assignments in unstructured logs. Our approach is motivated by a modular enterprise system without predefined workflows, where dynamic processes generate low-level logs requiring interpretation. We validate our method on a semi-synthetic business dataset and a fully synthetic dataset from PLG2. Our results demonstrate that trace segmentation improves process discovery, aligns logs with meaningful structures, and significantly enhances process mining in unstructured environments.

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