Abstract
Outlier detection in process mining refers to either infrequent behavior in relation to the underlying business process models or to anomalous latencies of task execution (temporal anomalies). In this work, we focus on the latter form of anomalies and we propose distance-based methods. Compared to solutions relying on probability distribution analysis and based on the experimental evaluation presented, our proposal is shown to be capable of covering both trace and event outliers, and being more efficient and effective. More specifically, running times of our technique are lower by up to an order of magnitude, while we achieve significantly higher precision and recall.
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Notes
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If the logs contain the start and end finish time of each task explicitly, then our approach to detecting latency anomalies can be applied to detecting anomalous task durations in a straightforward manner.
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It is out of our scope in this work to compare R-tree vs M-tree.
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Acknowledgment
The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 1052).
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Mavroudopoulos, I., Gounaris, A. (2020). Detecting Temporal Anomalies in Business Processes Using Distance-Based Methods. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham. https://doi.org/10.1007/978-3-030-61527-7_40
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