Anomaly Detection and Explanation Discovery on Event Streams

Fei Song 1 Boyao Zhou 2 Quan Sun 2 Wang Sun 2 Shiwen Xia 2 Yanlei Diao 1
1 CEDAR - Rich Data Analytics at Cloud Scale
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
Abstract : As enterprise information systems are collecting event streams from various sources, the ability of a system to automatically detect anomalous events and further provide human readable explanations is of paramount importance. In this position paper, we argue for the need of a new type of data stream analytics that can address anomaly detection and explanation discovery in a single, integrated system, which not only offers increased business intelligence, but also opens up opportunities for improved solutions. In particular , we propose a two-pass approach to building such a system, highlight the challenges, and offer initial directions for solutions.
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Fei Song, Boyao Zhou, Quan Sun, Wang Sun, Shiwen Xia, et al.. Anomaly Detection and Explanation Discovery on Event Streams. BIRTE2018, Aug 2018, RIO, Brazil. ⟨hal-01970660⟩

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