Data Mining for the Internet of Things: Literature Review and Challenges

Data Mining for the Internet of Things: Literature Review and Challenges

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application...

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Main Authors: Feng Chen, Pan Deng, Jiafu Wan, Daqiang Zhang, Athanasios V. Vasilakos, Xiaohui Rong
Format: Article
Language:English
Published: SAGE Publishing 2015-08-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2015/431047
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spelling doaj-603fb20a86714580a7506974149a03372020-11-25T03:43:39ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/431047431047Data Mining for the Internet of Things: Literature Review and ChallengesFeng Chen0Pan Deng1Jiafu Wan2Daqiang Zhang3Athanasios V. Vasilakos4Xiaohui Rong5 Guiyang Academy of Information Technology, Guiyang 550000, China Parallel Computing Laboratory, Institute of Software Chinese Academy of Sciences, Beijing 100190, China School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China School of Software Engineering, Tongji University, Shanghai 201804, China Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden Chinese Academy of Civil Aviation Science and Technology, Beijing 100028, ChinaThe massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.https://doi.org/10.1155/2015/431047
collection DOAJ
language English
format Article
sources DOAJ
author Feng Chen
Pan Deng
Jiafu Wan
Daqiang Zhang
Athanasios V. Vasilakos
Xiaohui Rong
spellingShingle Feng Chen
Pan Deng
Jiafu Wan
Daqiang Zhang
Athanasios V. Vasilakos
Xiaohui Rong
Data Mining for the Internet of Things: Literature Review and Challenges
International Journal of Distributed Sensor Networks
author_facet Feng Chen
Pan Deng
Jiafu Wan
Daqiang Zhang
Athanasios V. Vasilakos
Xiaohui Rong
author_sort Feng Chen
title Data Mining for the Internet of Things: Literature Review and Challenges
title_short Data Mining for the Internet of Things: Literature Review and Challenges
title_full Data Mining for the Internet of Things: Literature Review and Challenges
title_fullStr Data Mining for the Internet of Things: Literature Review and Challenges
title_full_unstemmed Data Mining for the Internet of Things: Literature Review and Challenges
title_sort data mining for the internet of things: literature review and challenges
publisher SAGE Publishing
series International Journal of Distributed Sensor Networks
issn 1550-1477
publishDate 2015-08-01
description The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.
url https://doi.org/10.1155/2015/431047
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AT jiafuwan dataminingfortheinternetofthingsliteraturereviewandchallenges
AT daqiangzhang dataminingfortheinternetofthingsliteraturereviewandchallenges
AT athanasiosvvasilakos dataminingfortheinternetofthingsliteraturereviewandchallenges
AT xiaohuirong dataminingfortheinternetofthingsliteraturereviewandchallenges
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