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dc.contributor.authorZaarour, Tarek
dc.contributor.authorPavlopoulou, Niki
dc.contributor.authorHasan, Souleiman
dc.contributor.authorul Hassan, Umair
dc.contributor.authorCurry, Edward
dc.identifier.citationTarek 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.
dc.description.abstractWith 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.en_IE
dc.publisherAssociation for Computing Machinery ACMen_IE
dc.relation.ispartofProceedings of the 11th ACM International Conference on Distributed and Event-based Systemsen
dc.subjectEvent-based processingen_IE
dc.subjectAnomaly detectionen_IE
dc.subjectEvent orderingen_IE
dc.subjectMarkov chain modelen_IE
dc.titleGrand challenge: Automatic anomaly detection over sliding windowsen_IE
dc.typeConference Paperen_IE
dc.local.contactUmair Ul Hassan. Email:

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