Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

Fast query of Big Data Based on rich Network Distribution Monitoring Information Flow

Authors
Zhijian Qu, Liang Zhao, Mingming Fan
Corresponding Author
Zhijian Qu
Available Online June 2017.
DOI
https://doi.org/10.2991/caai-17.2017.93How to use a DOI?
Keywords
rich network applications; MPP query
Abstract
In view of the inefficient query efficiency of mass monitoring data in distribution network, a new method for fast querying large data applications with rich network is proposed. Using the MPP query engine, the distribution data is embedded into the network monitoring interface, and the asynchronous query of the cross platform query interface is realized by using Ajax asynchronous interaction. The test results show that the cluster query, MPP combined with asynchronous callback mechanism to interactive server query update to hundreds of MS, cluster CPU use rate of about 11%, a reasonable amount can improve the clustering performance, improve the response ability of mass data, but much less than the expansion to enhance the ability of response to mass data cluster scale.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/caai-17.2017.93How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhijian Qu
AU  - Liang Zhao
AU  - Mingming Fan
PY  - 2017/06
DA  - 2017/06
TI  - Fast query of Big Data Based on rich Network Distribution Monitoring Information Flow
BT  - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 411
EP  - 414
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.93
DO  - https://doi.org/10.2991/caai-17.2017.93
ID  - Qu2017/06
ER  -