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Anomaly Detection and Explanation Discovery on Event Streams

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Fei Song
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Boyao Zhou
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Quan Sun
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Wang Sun
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Shiwen Xia
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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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Dates and versions

hal-01970660 , version 1 (05-01-2019)

Identifiers

  • HAL Id : hal-01970660 , version 1

Cite

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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