@inproceedings{dda3ebc06375446f98e8c6a74b19c35a,
title = "Grand challenge: Flinkman - Anomaly detection in manufacturing equipment with apache flink",
abstract = "We present a (soft) real-time event-based anomaly detection application for manufacturing equipment, built on top of the general purpose stream processing framework Apache Flink. The 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. The main goal is to reduce end-to-end latencies, while handling high input throughput and still provide exact results. Given a truly distributed setting, this challenge also entails careful task and/or data parallelization and balancing. We propose FlinkMan, a system that offers a generic and efficient solution, which maximizes the usage of available cores and balances the load among them. We illustrates the accuracy and efficiency of FlinkMan, over a 3-step pipelined data stream analysis, that includes clustering, modeling and querying.",
keywords = "Anomaly detection, Clustering, Linked-Data, Markov chains, Stream processing",
author = "Nicolo Rivetti and Yann Busnel and Avigdor Gal",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017 ; Conference date: 19-06-2017 Through 23-06-2017",
year = "2017",
month = jun,
day = "8",
doi = "https://doi.org/10.1145/3093742.3095099",
language = "الإنجليزيّة",
series = "DEBS 2017 - Proceedings of the 11th ACM International Conference on Distributed Event-Based Systems",
pages = "274--279",
booktitle = "DEBS 2017 - Proceedings of the 11th ACM International Conference on Distributed Event-Based Systems",
}