Grand challenge: Automatic anomaly detection over sliding windows
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Date
2017-06-19Author
Zaarour, Tarek
Pavlopoulou, Niki
Hasan, Souleiman
ul Hassan, Umair
Curry, Edward
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Recommended Citation
Tarek Zaarour, Niki Pavlopoulou, Souleiman Hasan, Umair ul Hassan,
and Edward Curry. 2017. Grand Challenge: Automatic Anomaly Detection
over Sliding Windows. In Proceedings of DEBS ’17, Barcelona, Spain,
June 19-23, 2017, 5 pages.
https://doi.org/10.1145/3093742.3095105
Published Version
Abstract
With the advances in the Internet of Things and rapid generation of
vast amounts of data, there is an ever growing need for leveraging
and evaluating event-based systems as a basis for building realtime
data analytics applications. The ability to detect, analyze, and
respond to abnormal patterns of events in a timely manner is as challenging
as it is important. For instance, distributed processing environment
might affect the required order of events, time-consuming
computations might fail to scale, or delays of alarms might lead
to unpredicted system behavior. The ACM DEBS Grand Challenge
2017 focuses on real-time anomaly detection for manufacturing
equipments based on the observation of a stream of measurements
generated by embedded digital and analogue sensors. In this paper,
we present our solution to the challenge leveraging the Apache
Flink stream processing framework and anomaly ordering based on
sliding windows, and evaluate the performance in terms of event
latency and throughput.