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Communication Dans Un Congrès Année : 2017

FlinkMan : Anomaly Detection in Manufacturing Equipment with Apache Flink : Grand Challenge

Résumé

We present a (soo) real-time event-based anomaly detection application for manufacturing equipment, built on top of the general purpose stream processing framework Apache Flink. e anomaly detection involves multiple CPUs and/or memory intensive tasks, such as clustering on large time-based window and parsing input data in RDF-format. e main goal is to reduce end-to-end latencies, while handling high input throughput and still provide exact results. Given a truly distributed seeing, this challenge also entails careful task and/or data parallelization and balancing. We propose FlinkMan, a system that ooers a generic and eecient solution , which maximizes the usage of available cores and balances the load among them. We illustrates the accuracy and eeciency of FlinkMan, over a 3-step pipelined data stream analysis, that includes clustering, modeling and querying.
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Dates et versions

hal-01644417 , version 1 (22-11-2017)

Identifiants

Citer

Yann Busnel, Nicolo Riveei, Avigdor Gal. FlinkMan : Anomaly Detection in Manufacturing Equipment with Apache Flink : Grand Challenge. DEBS '17 : 11th ACM International Conference on Distributed and Event-based Systems, Jun 2017, Barcelone, Spain. pp.274-279 ⟨10.1145/3093742.3095099⟩. ⟨hal-01644417⟩
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