Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

An Adaptive Algorithm of Data Network Traverse Based on Node Analysis

Authors
Yan XU, Yi ZHENG, Yu ZHANG, Hui LIU, Hao CUI, Hai-lin LIU
Corresponding Author
Yan XU
Available Online December 2016.
DOI
https://doi.org/10.2991/cnct-16.2017.8How to use a DOI?
Keywords
Data Network, Ternary Tree, Traverse.
Abstract

In this paper we describe a data network traverse algorithm. It traverses based on ternary tree and has Depth First Search (DFS) feature. The data network modeling employs three types of components: Line, Node and Intersection. Each type has typical data structure to connect other type. This algorithm is self-adaptable for random data network and could achieve dynamic analysis with high speed and accuracy. It could be widely used in modeling, analysis and inspection of industrial pipeline such as circuit, water path and gas path.

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 International Conference on Computer Networks and Communication Technology (CNCT 2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-301-2
ISSN
2352-538X
DOI
https://doi.org/10.2991/cnct-16.2017.8How 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  - Yan XU
AU  - Yi ZHENG
AU  - Yu ZHANG
AU  - Hui LIU
AU  - Hao CUI
AU  - Hai-lin LIU
PY  - 2016/12
DA  - 2016/12
TI  - An Adaptive Algorithm of Data Network Traverse Based on Node Analysis
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 56
EP  - 61
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.8
DO  - https://doi.org/10.2991/cnct-16.2017.8
ID  - XU2016/12
ER  -