Proceedings of the 4th International Conference on Information Technology and Management Innovation

A Research on Machine Learning Methods for Big Data Processing

Authors
Junfei Qiu, Youming Sun
Corresponding Author
Junfei Qiu
Available Online October 2015.
DOI
10.2991/icitmi-15.2015.155How to use a DOI?
Keywords
Machine learning; Big data; Data mining; Cloud computing
Abstract

Machine learning has found widespread implementations and applications in many different domains in our life. However, as the big data era is coming, some traditional machine learning techniques cannot satisfy the requirements of real-time processing for large volumes of data. In response, machine learning needs to reinvent itself for big data. In this article, we provide a review of machine learning for big data processing in recent studies. Firstly, a discussion about big data is presented, followed by the analysis of the new characteristics of machine learning in the context of big data. Then, we propose a feasible reference framework for dealing with big data based on machine learning techniques. Finally, several research challenges and open issues are addressed.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 4th International Conference on Information Technology and Management Innovation
Series
Advances in Computer Science Research
Publication Date
October 2015
ISBN
978-94-6252-112-4
ISSN
2352-538X
DOI
10.2991/icitmi-15.2015.155How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Junfei Qiu
AU  - Youming Sun
PY  - 2015/10
DA  - 2015/10
TI  - A Research on Machine Learning Methods for Big Data Processing
BT  - Proceedings of the 4th International Conference on Information Technology and Management Innovation
PB  - Atlantis Press
SP  - 920
EP  - 928
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitmi-15.2015.155
DO  - 10.2991/icitmi-15.2015.155
ID  - Qiu2015/10
ER  -