Large Scale Hierarchical Classification Framework for Network Big Data

DOI

10.2991/icsmim-15.2016.74How to use a DOI?

Keywords

big data, hierarchical classification, data collection

Abstract

With the development of Internet technology, Internet data growth rapidly and become big data. According to the different properties of the network big data, network big data classification is the foundation of many network applications, including network data management, green Internet, network bandwidth usage category management, network reputation management, security filtering and so on. Due to the variety and the large scale of network data, the traditional classification methods can’t effectively solve the problem of network big data classification. In this paper, we design and implement a large scale hierarchical classification framework (LSHC) for network big data, including self-feedback system architecture, multi-dimensional network big data classification standard, active and passive combining network big data collection technology, automatic self-correction network big data classification techniques. This framework offers a promising approach for large-scale real-time network big data classification system.

Copyright

© 2016, 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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TY  - CONF
AU  - Weihong Han
AU  - Zizhong Huang
AU  - Yan Jia
PY  - 2016/01
DA  - 2016/01
TI  - Large Scale Hierarchical Classification Framework for Network Big Data
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 392
EP  - 396
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.74
DO  - 10.2991/icsmim-15.2016.74
ID  - Han2016/01
ER  -