骄傲:并行异常检测流

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI:10.1145/3318464.3384688

Theodoros Toliopoulos, Christos Bellas, A. Gounaris, A. Papadopoulos

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引用次数: 10

摘要

我们介绍了PROUD，即PaRallel OUtlier Detection for streams，这是一个可扩展的引擎，用于为大数据流量身定制的连续多参数并行的基于距离的异常(或异常)检测。PROUD是建立在Flink之上的。它为数据摄取定义了一个简单的API。它支持多种并行技术，包括新的并行技术，可以轻松配置的连续异常值检测。此外，它以图形方式报告感兴趣的指标，并将主要结果存储到永久存储中，以便将来进行分析。它可以很容易地扩展以支持其他技术。最后，它以开放源代码的形式公开提供。

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PROUD: PaRallel OUtlier Detection for Streams

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.

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Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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