Exploring and cleaning big data with random sample data blocks-Reference-Cited by-同舟云学术

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Exploring and cleaning big data with random sample data blocks

Published:2019-06-04 Issue:1 Volume:6 Page:
ISSN:2196-1115
Container-title:Journal of Big Data
language:en
Short-container-title:J Big Data

Author:

Salloum Salman^ORCID,Huang Joshua Zhexue,He Yulin

Funder

National Natural Science Foundation of China

National Key R&D Program of China

China Postdoctoral Science Foundation

Scientific Research Foundation of Shenzhen University for Newly-introduced Teachers

Publisher

Springer Science and Business Media LLC

Subject

Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Link

http://link.springer.com/content/pdf/10.1186/s40537-019-0205-4.pdf

Reference59 articles.

1. Nair R. Big data needs approximate computing: technical perspective. Commun ACM. 2014;58(1):104. https://doi.org/10.1145/2688072 .

2. Goiri Í, Bianchini R, Nagarakatte S, Nguyen TD. Approxhadoop: bringing approximations to mapreduce frameworks. In: Proceedings of the twentieth international conference on architectural support for programming languages and operating systems. ACM; 2015. p. 383–97.

3. Salloum S, Huang JZ, He Y. Empirical analysis of asymptotic ensemble learning for big data. In: Proceedings of the 3rd IEEE/ACM international conference on big data computing, applications and technologies. BDCAT ’16, ACM, New York, NY, USA; 2016, p. 8–17. https://doi.org/10.1145/3006299.3006306 .

4. Kwon BC, Verma J, Haas PJ, Demiralp. Sampling for scalable visual analytics. IEEE Comput Graph Appl. 2017;37(1):100–8. https://doi.org/10.1109/MCG.2017.6 .

5. Riondato M. Sampling-based data mining algorithms: modern techniques and case studies. In: Proceedings of the 2014th European conference on machine learning and knowledge discovery in databases-volume part III. ECMLPKDD’14, Berlin, Heidelberg: Springer; 2014, p. 516–9.

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