Grand challenge: Automatic anomaly detection over sliding windows
Zaarour, Tarek Pavlopoulou, Niki Hasan, Souleiman ul Hassan, Umair Curry, Edward
Zaarour, Tarek
Pavlopoulou, Niki
Hasan, Souleiman
ul Hassan, Umair
Curry, Edward
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Repository DOI
Publication Date
2017-06-19
Type
Conference Paper
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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
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.
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Publisher
Association for Computing Machinery ACM
Publisher DOI
10.1145/3093742.3095105
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland