Extracting sample data based on poisson distribution

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

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

7 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 languageEnglish
Title of host publicationProceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages374-378
Number of pages5
ISBN (Electronic)9781538604069
DOIs
Publication statusPublished - 14 Nov 2017
Event16th International Conference on Machine Learning and Cybernetics, ICMLC 2017 - Ningbo, China
Duration: 9 Jul 201712 Jul 2017

Publication series

NameProceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017
Volume2

Conference

Conference16th International Conference on Machine Learning and Cybernetics, ICMLC 2017
Country/TerritoryChina
CityNingbo
Period9/07/1712/07/17

User-Defined Keywords

  • Big data
  • Data processing
  • Data sampling
  • Poisson distribution

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