by Tarek Zaarour, Niki Pavlopoulou, Souleiman Hasan, Umair ul Hassan, Edward Curry
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.
Reference:
Tarek Zaarour, Niki Pavlopoulou, Souleiman Hasan, Umair ul Hassan, Edward Curry, "Automatic Anomaly Detection over Sliding Windows", In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems, ACM, New York, NY, USA, pp. 310-314, 2017.
Bibtex Entry:
@inproceedings{TarekZaarourNikiPavlopoulouSouleimanHasan2017,
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.},
address = {New York, NY, USA},
author = {Zaarour, Tarek and Pavlopoulou, Niki and Hasan, Souleiman and ul Hassan, Umair and Curry, Edward},
booktitle = {Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems},
doi = {10.1145/3093742.3095105},
file = {:Users/ed/Library/Application Support/Mendeley Desktop/Downloaded/Zaarour et al. - 2017 - Automatic Anomaly Detection over Sliding Windows.pdf:pdf},
isbn = {9781450350655},
keywords = {K-means,Markov chain model,anomaly detection,event ordering,event-based processing},
month = {jun},
pages = {310--314},
publisher = {ACM},
title = {{Automatic Anomaly Detection over Sliding Windows}},
url = {http://www.edwardcurry.org/publications/DEBS2017-Zaarour.pdf},
year = {2017}
}