A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams

Abinash Borah, Le Gruenwald, Eleazar Leal, Egawati Panjei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

A data stream is an infinite sequence of data points generated from a source continuously at a fast rate, which is characterized by the transiency of the data points, the temporal relationship among the data points, concept drift, and multi-dimensionality of data points. Outlier detection in data streams thus needs to deal with the characteristics of Big Data applications such as volume, velocity, and variety. The problem of detecting outliers in multiple concurrent data streams introduces additional challenges to the problem. In this paper, we propose a parallel outlier detection technique CODS to detect Contextual Outliers in multiple concurrent independent multi-dimensional Data Streams using a Graphics Processing Unit (GPU). The proposed algorithm addresses all the aforesaid characteristics of data streams. A set of experiments demonstrates reasonable outlier detection accuracy and scalability of CODS with the number of data streams.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2737-2742
Number of pages6
ISBN (Electronic)9781665439022
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: Dec 15 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/15/2112/18/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work is supported in part by the National Foundation under Grant No. 1302439 and 1302423.

Funding Information:
This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Contextual Outlier
  • Data Stream
  • GPU
  • Outlier Detection

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