Online anomaly detection using statistical leverage for streaming business process events
It addresses the need for real-time anomaly detection in business processes to enable prompt corrective actions, representing an incremental advancement in applying existing statistical methods to a new domain.
The paper tackles the problem of detecting anomalies in streaming business process events online, which was previously lacking, by introducing a novel approach using statistical leverage, achieving evaluation on both artificial and real event streams.
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.