@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,
day = "14",
doi = "10.1109/ICMLC.2017.8108950",
language = "English",
series = "Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017",
publisher = "IEEE",
pages = "374--378",
booktitle = "Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017",
address = "United States",
}
