Research on Distributed Data Stream Mining in Internet of Things

DOI

10.2991/lemcs-14.2014.35How to use a DOI?

Keywords

Internet of Things; cloud computing; MapReduce model; distributed-mining;data stream;data mine

Abstract

For mining useful data from mass data generated by Internet of things, analyses shortages of the traditional Apriori algorithm which has a lower mining efficiency and occupies the larger memory space. So, MapReduce model of cloud computing is introduced. In the mechanism of MapReduce, combine the architecture characteristics and key technology of Internet of Things, conduct distributed- mining on data and information in environment of the Internet of Things, and the calculation model of distributed data stream mining is drawn. Performance analysis proves that the new data stream mining model overcomes difficulty of traditional data mining, and each link reflects the idea of distributed structure and improves mining efficiency obviously.

Copyright

© 2014, 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  - Liancheng Xu
AU  - Jiao Xun
PY  - 2014/05
DA  - 2014/05
TI  - Research on Distributed Data Stream Mining in Internet of Things
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 149
EP  - 154
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-14.2014.35
DO  - 10.2991/lemcs-14.2014.35
ID  - Xu2014/05
ER  -