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

Abinash Borah; Le Gruenwald; Eleazar Leal; Egawati Panjei

doi:10.1109/BigData52589.2021.9671460

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

Abinash Borah, Le Gruenwald, Eleazar Leal, Egawati Panjei

Computer Science (Duluth)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 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 language	English (US)
Title of host publication	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
Editors	Yixin 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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2737-2742
Number of pages	6
ISBN (Electronic)	9781665439022
DOIs	https://doi.org/10.1109/BigData52589.2021.9671460
State	Published - 2021
Event	2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference	2021 IEEE International Conference on Big Data, Big Data 2021
Country/Territory	United States
City	Virtual, Online
Period	12/15/21 → 12/18/21

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

Publisher link

10.1109/BigData52589.2021.9671460

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Borah, A., Gruenwald, L., Leal, E., & Panjei, E. (2021). A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. T. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 (pp. 2737-2742). (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData52589.2021.9671460

A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams. / Borah, Abinash; Gruenwald, Le; Leal, Eleazar et al.
Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. ed. / Yixin 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. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2737-2742 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Borah, A, Gruenwald, L, Leal, E & Panjei, E 2021, A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams. in Y Chen, H Ludwig, Y Tu, U Fayyad, X Zhu, XT Hu, S Byna, X Liu, J Zhang, S Pan, V Papalexakis, J Wang, A Cuzzocrea & C Ordonez (eds), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2737-2742, 2021 IEEE International Conference on Big Data, Big Data 2021, Virtual, Online, United States, 12/15/21. https://doi.org/10.1109/BigData52589.2021.9671460

Borah A, Gruenwald L, Leal E, Panjei E. A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams. In Chen Y, Ludwig H, Tu Y, Fayyad U, Zhu X, Hu XT, Byna S, Liu X, Zhang J, Pan S, Papalexakis V, Wang J, Cuzzocrea A, Ordonez C, editors, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2737-2742. (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). doi: 10.1109/BigData52589.2021.9671460

Borah, Abinash ; Gruenwald, Le ; Leal, Eleazar et al. / A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. editor / Yixin 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. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2737-2742 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

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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.",

keywords = "Contextual Outlier, Data Stream, GPU, Outlier Detection",

author = "Abinash Borah and Le Gruenwald and Eleazar Leal and Egawati Panjei",

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: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",

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doi = "10.1109/BigData52589.2021.9671460",

language = "English (US)",

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pages = "2737--2742",

editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, {Xiaohua Tony} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",

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AU - Leal, Eleazar

AU - Panjei, Egawati

N1 - 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.

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Y1 - 2021

N2 - 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.

AB - 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.

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A2 - Zhu, Xingquan

A2 - Hu, Xiaohua Tony

A2 - Byna, Suren

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ER -

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

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