Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)

Research on Industry Data Analysis Model Based on Hadoop Big Data Platform

Authors
Xu Hongsheng, Fan Ganglong, Li Ke
Corresponding Author
Xu Hongsheng
Available Online June 2016.
DOI
10.2991/icemc-17.2017.159How to use a DOI?
Keywords
Big data; Hadoop; Industry data analysis model; MapReduce; Potential valuable information
Abstract

Big data analysis refers to the huge size of data analysis, from a large amount of data to extract potential valuable information. Hadoop, the core of the Hadoop distributed file system and MapReduce, provides users with the underlying, detailed, transparent distributed infrastructure. In this paper, a business model statistic system based on big data is analyzed. This paper describes the traditional industry data analysis model based on big data technology, and puts forward the existing problems. The paper presents research on industry data analysis model based on Hadoop big data platform.

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/).

Download article (PDF)

Volume Title
Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)
Series
Advances in Computer Science Research
Publication Date
June 2016
ISBN
10.2991/icemc-17.2017.159
ISSN
2352-538X
DOI
10.2991/icemc-17.2017.159How 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  - Xu Hongsheng
AU  - Fan Ganglong
AU  - Li Ke
PY  - 2016/06
DA  - 2016/06
TI  - Research on Industry Data Analysis Model Based on Hadoop Big Data Platform
BT  - Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)
PB  - Atlantis Press
SP  - 783
EP  - 787
SN  - 2352-538X
UR  - https://doi.org/10.2991/icemc-17.2017.159
DO  - 10.2991/icemc-17.2017.159
ID  - Hongsheng2016/06
ER  -