  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.
