Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)

Novel Application of DaaS and Hadoop Technology in Big Data Cloud Computing Platform

Authors
Hongsheng Xu, Ganglong Fan, Ke Li
Corresponding Author
Hongsheng Xu
Available Online December 2017.
DOI
10.2991/mcei-17.2017.81How to use a DOI?
Keywords
Cloud computing; DaaS; Hadoop; Big data; Software framework
Abstract

DaaS is to dig out the potential value of big data and provide services according to the needs of users. Hadoop is a software framework for distributed processing of large amounts of data, and in a reliable, efficient, scalable processing way, relying on the horizontal expansion, to improve the computing and storage capacity by increasing the cheap commercial servers. The paper presents novel application of DaaS and Hadoop technology in big data cloud computing platform. Users can easily in the application on the development and operation of big data processing.

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 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
Series
Advances in Computer Science Research
Publication Date
December 2017
ISBN
10.2991/mcei-17.2017.81
ISSN
2352-538X
DOI
10.2991/mcei-17.2017.81How 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  - Hongsheng Xu
AU  - Ganglong Fan
AU  - Ke Li
PY  - 2017/12
DA  - 2017/12
TI  - Novel Application of DaaS and Hadoop Technology in Big Data Cloud Computing Platform
BT  - Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
PB  - Atlantis Press
SP  - 373
EP  - 377
SN  - 2352-538X
UR  - https://doi.org/10.2991/mcei-17.2017.81
DO  - 10.2991/mcei-17.2017.81
ID  - Xu2017/12
ER  -