Abstract
Event logs can be analyzed using various process mining techniques (e.g., process discovery) to obtain valuable information about the actual behavior of business process executions. Typically, these techniques rely on the presence of a case identifier linking events to process instances. However, if the process involves information systems that do not record events in a process-oriented manner, a clear case identifier may be missing, resulting in an unlabeled event log. While some approaches already address the challenge of inferring case identifiers for unlabeled event logs, most of them provide limited support for cyclic behavior without additional inputs. This paper proposes a three-step approach to correlate events with case identifiers for unlabeled event logs originating from processes with cyclic behavior. While evaluating the accuracy of our approach with two real-world event logs (MIMIC-IV and Road Traffic Fine Management), we show that our approach, compared to the existing ones, detects cyclic behavior and correlates events closer to the original process instances without additional inputs.
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Lichtenstein, T., Bano, D., Weske, M. (2022). Attribute-Driven Case Notion Discovery for Unlabeled Event Logs. In: Marrella, A., Weber, B. (eds) Business Process Management Workshops. BPM 2021. Lecture Notes in Business Information Processing, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-030-94343-1_9
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DOI: https://doi.org/10.1007/978-3-030-94343-1_9
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