Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

The Big Data-Driven Industrial CPS Real-Time Processing System Based on Storm

Authors
Junyu Ye, Lichen Zhang
Corresponding Author
Junyu Ye
Available Online May 2018.
DOI
10.2991/ncce-18.2018.125How to use a DOI?
Keywords
Big Data, Real-time processing, CPS, Storm, Industrial system, Intelligent.
Abstract

In industry 4.0, CPS will play a huge role in the industrial production and supply management [1-3]. CPS makes the industrial system more intelligent, making the subsequent salesbusinesses more data-oriented and intelligent. However, the industry environment produces large of data during the production process. How to deal with these huge data quickly and analyze meaningful value message becomes the core issue of CPS. In this paper, a real - time processing system of CPS for industrial management system is proposed. The architecture is based on the characteristics of industrial management system and designed for solving the big stream data processing.

Copyright
© 2018, 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 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
Series
Advances in Intelligent Systems Research
Publication Date
May 2018
ISBN
978-94-6252-517-7
ISSN
1951-6851
DOI
10.2991/ncce-18.2018.125How to use a DOI?
Copyright
© 2018, 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  - Junyu Ye
AU  - Lichen Zhang
PY  - 2018/05
DA  - 2018/05
TI  - The Big Data-Driven Industrial CPS Real-Time Processing System Based on Storm
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 761
EP  - 764
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.125
DO  - 10.2991/ncce-18.2018.125
ID  - Ye2018/05
ER  -