Optimizing the confidence bound of count-min sketches to estimate the streaming big data query results more precisely-Reference-Cited by-同舟云学术

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Optimizing the confidence bound of count-min sketches to estimate the streaming big data query results more precisely

Published:2019-01-02 Issue:6 Volume:102 Page:1419-1445
ISSN:0010-485X
Container-title:Computing
language:en
Short-container-title:Computing

Author:

Guo Ruixin,Xue Erkang,Zhang Feng^ORCID,Zhao Gansen,Qu Guangzhi

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Fundamental Research Founds for National University, China University of Geosciences, Wuhan

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Numerical Analysis,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00607-018-00695-z.pdf

Reference29 articles.

1. Chen D, Wang L, Xiaomin W, Chen J, Khan SU, Koodziej J, Tian M, Huang F, Liu W (2013) Hybrid modelling and simulation of huge crowd over a hierarchical grid architecture. Future Gener Comput Syst 29(5):1309–1317

2. Cormode G (2009) Count-min sketch. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, pp 511–516. https://doi.org/10.1007/978-0-387-39940-9_87

3. Cormode G, Muthukrishnan S (2005) An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1):58–75

4. Deng Z, Wu X, Wang L, Chen X, Ranjan R, Zomaya A, Chen D (2015) Parallel processing of dynamic continuous queries over streaming data flows. IEEE Trans Parallel Distrib Syst 26(3):834–846

5. Deng Z, Han W, Wang L, Ranjan R, Zomaya AY, Jie W (2017) An efficient online direction-preserving compression approach for trajectory streaming data. Future Gener Comput Syst 68:150–162

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mille Cheval: a GPU-based in-memory high-performance computing framework for accelerated processing of big-data streams;The Journal of Supercomputing;2021-01-04

2. Software-driven big data analytics;Computing;2020-06

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