PROUD: PaRallel OUtlier Detection for streams

被引：8

作者：

Toliopoulos, Theodoros ^{[1
]}

Bellas, Christos ^{[1
]}

Gounaris, Anastasios ^{[1
]}

Papadopoulos, Apostolos ^{[1
]}

机构：

[1] Aristotle Univ Thessaloniki, Thessaloniki, Greece

来源：

SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA | 2020年

基金：

欧盟地平线“2020”;

关键词：

D O I：

10.1145/3318464.3384688

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We introduce PROUD, standing for PaRallel OUtlier Detection for streams, which is an extensible engine for continuous multi-parameter parallel distance-based outlier (or anomaly) detection tailored to big data streams. PROUD is built on top of Flink. It defines a simple API for data ingestion. It supports a variety of parallel techniques, including novel ones, for continuous outlier detection that can be easily configured. In addition, it graphically reports metrics of interest and stores main results into a permanent store to enable future analysis. It can be easily extended to support additional techniques. Finally, it is publicly provided in open-source.

引用

页码：2717 / 2720

页数：4

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