xSemAD: Explainable Semantic Anomaly Detection in Event Logs Using Sequence-to-Sequence Models

Author:

Busch Kiran,Kampik Timotheus,Leopold Henrik

Publisher

Springer Nature Switzerland

Reference33 articles.

1. van der Aa, H., Rebmann, A., Leopold, H.: Natural language-based detection of semantic execution anomalies in event logs. Inf. Syst. 102, 101824 (2021)

2. Bergman, A., Rebmann, A., Kampik, T.: BPMN2constraints: breaking down BPMN diagrams into declarative process query constraints. In: BPM 2023, 21st International Conference on Business Process Management, Utrecht, The Netherlands, 11–15 September 2023, pp. 137–141. Aachen University (2023)

3. Berti, A., Van Zelst, S.J., van der Aalst, W.: Process Mining for Python (PM4Py): bridging the gap between process-and data science. arXiv preprint arXiv:1905.06169 (2019)

4. Bezerra, F., Wainer, J.: Anomaly detection algorithms in logs of process aware systems. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 951–952 (2008)

5. Bezerra, F., Wainer, J.: Algorithms for anomaly detection of traces in logs of process aware information systems. Inf. Syst. 38(1), 33–44 (2013)

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