Proceedings of the 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017)

Research on the Development of Data Scientific Analysis Tools in the Big Data Age

Authors
Yuxuan Xu
Corresponding Author
Yuxuan Xu
Available Online July 2017.
DOI
10.2991/essaeme-17.2017.410How to use a DOI?
Keywords
Data science, R language, big data
Abstract

According to the features of big data era, this paper analyzes the main challenges that massive data bring to the analysis tool of data science. The paper introduces the big data analysis tool in response to challenges. Then, the paper carries on the comparative analysis of R language, Rapid Miner and Mahout 3 popular analysis tools of big data in data science, which finds that R language and Rapid Miner have fully functions and the Mahout has more outstanding analysis capability of big data. Finally, the paper points out the development trend of data science analysis tool.

Copyright
© 2017, 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 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
July 2017
ISBN
10.2991/essaeme-17.2017.410
ISSN
2352-5398
DOI
10.2991/essaeme-17.2017.410How to use a DOI?
Copyright
© 2017, 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  - Yuxuan Xu
PY  - 2017/07
DA  - 2017/07
TI  - Research on the Development of Data Scientific Analysis Tools in the Big Data Age
BT  - Proceedings of the 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/essaeme-17.2017.410
DO  - 10.2991/essaeme-17.2017.410
ID  - Xu2017/07
ER  -