Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 108-112.doi: 10.11896/jsjkx.220300273

• Database & Big Data & Data Science • Previous Articles     Next Articles

Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment

CHEN Jing1, WU Ling-ling2   

  1. 1 CISDI Engineering Co.,Ltd,Chongqing 401122,China
    2 Chongqing Jiaotong University,Chongqing 400074,China
  • Received:2022-03-09 Revised:2022-05-25 Published:2022-08-02
  • About author:CHEN Jing,born in 1982,master,engineer.His main research interests include digitalization and informatization of engineering industry.
    WU Ling-ling,born in 1976,Ph.D,associate professor.Her main research interests include transport engineering and so on.
  • Supported by:
    National Key Research and Development Program of China(2018YFB1601001).

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Abstract: Current feature detection methods for big data of Internet of vehicles ignore the data attribute weight,resulting in low efficiency and fail to provide efficient services in vehicle operation.Therefore,a hybrid attribute feature detection method for big data of Internet of vehicles in multi-source heterogeneous environment is proposed.Middleware is used to build an integration model to integrate multi-source heterogeneous data of the Internet of vehicles,and standardization and attribute reduction of integrated data are completed.With pre-processed data as input,attribute features are extracted by weighted principal component analysis,and feature clustering is realized by clustering method to complete the feature detection of mixed attribute of Internet of vehicles big data.Experimental results show that compared with existing methods,the sensitivity index of the proposed method is higher and the time complexity is lower,which indicates that the proposed feature detection method is more efficient and can more accurately complete the feature extraction task of mixed attributes of the Internet of vehicles big data.

Key words: Feature detection method, Internet of vehicles big data, Mixed attributes, Multi-source heterogeneous environment

CLC Number: 

  • TP368.6
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