Extracting sample data based on poisson distribution

Hao Lan Zhang; Jiming LIU; Tongliang Li; Yun Xue; Songjie Xu; Junhua Chen

doi:10.1109/ICMLC.2017.8108950

Extracting sample data based on poisson distribution

Hao Lan Zhang
, Jiming LIU
, Tongliang Li
, Yun Xue
, Songjie Xu
, Junhua Chen

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

9 Citations (Scopus)

Abstract

Sampling methods are becoming in demand due to the rapid growth of big data applications. The term Big Data not only means the large size of data volume but also indicates the high speed of data generation, which plagues many existing data mining and analytic applications owing to the limited capability of processing large volume of data for real time analysis. Therefore, the demands for the use of sampling to generate summary data sets that support rapid queries are increasing according to Cormode and Duffield. The state-of-the art in sampling methods have been successfully applied to various areas including network traffic and social networks[1]. In this paper, a novel Poisson-based sampling method is introduced to provide a comprehensive data set for real time analysis. The proposed Poisson-based sampling method extends the previous Normal Distribution sampling method [2]. The experimental results show efficiency of the proposed method.

Original language	English
Title of host publication	Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017
Publisher	IEEE
Pages	374-378
Number of pages	5
ISBN (Electronic)	9781538604069
DOIs	https://doi.org/10.1109/ICMLC.2017.8108950
Publication status	Published - 14 Nov 2017
Event	16th International Conference on Machine Learning and Cybernetics, ICMLC 2017 - Ningbo, China Duration: 9 Jul 2017 → 12 Jul 2017

Publication series

Name	Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017
Volume	2

Conference

Conference	16th International Conference on Machine Learning and Cybernetics, ICMLC 2017
Country/Territory	China
City	Ningbo
Period	9/07/17 → 12/07/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

User-Defined Keywords

Big data
Data processing
Data sampling
Poisson distribution

Access to Document

10.1109/ICMLC.2017.8108950

Cite this

Zhang, H. L., LIU, J., Li, T., Xue, Y., Xu, S., & Chen, J. (2017). Extracting sample data based on poisson distribution. In Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017 (pp. 374-378). (Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017; Vol. 2). IEEE. https://doi.org/10.1109/ICMLC.2017.8108950

@inproceedings{b457a32043844cf29ae6dce2b521e02e,

title = "Extracting sample data based on poisson distribution",

abstract = "Sampling methods are becoming in demand due to the rapid growth of big data applications. The term Big Data not only means the large size of data volume but also indicates the high speed of data generation, which plagues many existing data mining and analytic applications owing to the limited capability of processing large volume of data for real time analysis. Therefore, the demands for the use of sampling to generate summary data sets that support rapid queries are increasing according to Cormode and Duffield. The state-of-the art in sampling methods have been successfully applied to various areas including network traffic and social networks[1]. In this paper, a novel Poisson-based sampling method is introduced to provide a comprehensive data set for real time analysis. The proposed Poisson-based sampling method extends the previous Normal Distribution sampling method [2]. The experimental results show efficiency of the proposed method.",

keywords = "Big data, Data processing, Data sampling, Poisson distribution",

author = "Zhang, \{Hao Lan\} and Jiming LIU and Tongliang Li and Yun Xue and Songjie Xu and Junhua Chen",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th International Conference on Machine Learning and Cybernetics, ICMLC 2017 ; Conference date: 09-07-2017 Through 12-07-2017",

year = "2017",

month = nov,

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doi = "10.1109/ICMLC.2017.8108950",

language = "English",

series = "Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017",

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booktitle = "Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017",

address = "United States",

}

Zhang, HL, LIU, J, Li, T, Xue, Y, Xu, S & Chen, J 2017, Extracting sample data based on poisson distribution. in Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017. Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017, vol. 2, IEEE, pp. 374-378, 16th International Conference on Machine Learning and Cybernetics, ICMLC 2017, Ningbo, China, 9/07/17. https://doi.org/10.1109/ICMLC.2017.8108950

Extracting sample data based on poisson distribution. / Zhang, Hao Lan; LIU, Jiming; Li, Tongliang et al.
Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017. IEEE, 2017. p. 374-378 (Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017; Vol. 2).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Extracting sample data based on poisson distribution

AU - Zhang, Hao Lan

AU - LIU, Jiming

AU - Li, Tongliang

AU - Xue, Yun

AU - Xu, Songjie

AU - Chen, Junhua

PY - 2017/11/14

Y1 - 2017/11/14

N2 - Sampling methods are becoming in demand due to the rapid growth of big data applications. The term Big Data not only means the large size of data volume but also indicates the high speed of data generation, which plagues many existing data mining and analytic applications owing to the limited capability of processing large volume of data for real time analysis. Therefore, the demands for the use of sampling to generate summary data sets that support rapid queries are increasing according to Cormode and Duffield. The state-of-the art in sampling methods have been successfully applied to various areas including network traffic and social networks[1]. In this paper, a novel Poisson-based sampling method is introduced to provide a comprehensive data set for real time analysis. The proposed Poisson-based sampling method extends the previous Normal Distribution sampling method [2]. The experimental results show efficiency of the proposed method.

AB - Sampling methods are becoming in demand due to the rapid growth of big data applications. The term Big Data not only means the large size of data volume but also indicates the high speed of data generation, which plagues many existing data mining and analytic applications owing to the limited capability of processing large volume of data for real time analysis. Therefore, the demands for the use of sampling to generate summary data sets that support rapid queries are increasing according to Cormode and Duffield. The state-of-the art in sampling methods have been successfully applied to various areas including network traffic and social networks[1]. In this paper, a novel Poisson-based sampling method is introduced to provide a comprehensive data set for real time analysis. The proposed Poisson-based sampling method extends the previous Normal Distribution sampling method [2]. The experimental results show efficiency of the proposed method.

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KW - Data processing

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ER -

Extracting sample data based on poisson distribution

Abstract

Publication series

Conference

UN SDGs

User-Defined Keywords

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