Online anomaly detection using statistical leverage for streaming business process events
Abstract
While several techniques for detecting trace-level anomalies in event logs in offline settings have appeared recently in the literature, such techniques are currently lacking for online settings. Event log anomaly detection in online settings can be crucial for discovering anomalies in process execution as soon as they occur and, consequently, allowing to promptly take early corrective actions. This paper describes a novel approach to event log anomaly detection on event streams that uses statistical leverage. Leverage has been used extensively in statistics to develop measures to identify outliers and it has been adapted in this paper to the specific scenario of event stream data. The proposed approach has been evaluated on both artificial and real event streams.
Keywords
Cite
@article{arxiv.2103.00831,
title = {Online anomaly detection using statistical leverage for streaming business process events},
author = {Jonghyeon Ko and Marco Comuzzi},
journal= {arXiv preprint arXiv:2103.00831},
year = {2021}
}
Comments
12 pages, 4 figures, conference (Proceedings of the 1st International Workshop on Streaming Analytics for Process Mining (SA4PM 2020) in conjunction with International Conference on Process Mining, Accepted for publication (Sep 2020))