Data Mining for the Internet of Things: Literature Review and Challenges
作者: Feng ChenPan DengJiafu WanDaqiang ZhangAthanasios V. VasilakosXiaohui RongHoubing Song
作者单位: 1Parallel Computing Laboratory, Institute of Software Chinese Academy of Sciences, Beijing 100190, China
2Guiyang Academy of Information Technology, Guiyang 550000, China
3School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
4School of Software Engineering, Tongji University, Shanghai 201804, China
5Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
6Chinese Academy of Civil Aviation Science and Technology, Beijing 100028, China
刊名: International Journal of Distributed Sensor Networks, 2015, Vol.2015
来源数据库: Hindawi Journal
DOI: 10.1155/2015/431047
原始语种摘要: The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithmscan be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data miningin 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 challengesand open research issues. At last a suggested big data mining system is proposed.
全文获取路径: Hindawi 

  • Data 数据
  • mining 矿业
  • application 申请
  • knowledge 知识
  • outlier 老围层
  • clustering 聚类
  • Internet 国际互连网
  • technique 技术
  • analysis 分析
  • systematic 有系统的