Continuous outlier mining of streaming data in flink

作者：

Highlights：

• We explore a series of implementation alternatives, differing in the algorithmic features they employ.

• We provide thorough experimental evaluation results.

• Overall, we demonstrate how outlier mining can be achieved in an efficient and scalable manner through parallelism.

• We offer the source code as an open-source library .

摘要

•We explore a series of implementation alternatives, differing in the algorithmic features they employ.•We provide thorough experimental evaluation results.•Overall, we demonstrate how outlier mining can be achieved in an efficient and scalable manner through parallelism.•We offer the source code as an open-source library .

论文关键词：Distance-based outlier detection,Flink,Data streams

论文评审过程：Received 11 February 2019, Revised 23 March 2020, Accepted 23 May 2020, Available online 29 May 2020, Version of Record 6 June 2020.

论文官网地址：https://doi.org/10.1016/j.is.2020.101569