Zaarour, Tarek and Pavlopoulou, Niki and Hasan, Souleiman and ul Hassan, Umair and Curry, Edward (2017) Automatic Anomaly Detection over Sliding Windows: Grand Challenge. In: DEBS 17 the 11th ACM International Conference, June 19-23 2017, Barcelona, Spain.

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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.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	event-based processing; anomaly detection; event ordering; K-means; Markov chain model;
Academic Unit:	Faculty of Social Sciences > School of Business
Item ID:	16007
Depositing User:	Souleiman Hasan
Date Deposited:	30 May 2022 11:39
Refereed:	Yes
URI:	Organisation
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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