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).

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
[1]GUO J,KIM S,WYMEERSCH H,et al.Guest Editorial:Introduction to the Special Section on Machine Learning-Based Internet of Vehicles:Theory,Methodology,and Applications[J].IEEE Transactions on Vehicular Technology,2019,68(5):4105-4109.
[2]YAO Z H,JIANG Y S.Integrated Connected Vehicle Simulation Platform of Vissim and Python[J].Computer Simulation,2018,35(12):143-146,405.
[3]SARUMATHIY C K,GEETHA K,RAJAN C.Improvement in Hadoop performance using integrated feature extraction and machine learning algorithms[J].Soft Computing,2020,24(1):627-636.
[4]TAN X W.Feature Extraction Algorithm Based on Big DataDepth Residual Learning[J].Bulletin of Science and Techno-logy,2019,35(4):89-92.
[5]WU Y X,WU Q B,ZHU J Q.Data-driven wind speed forecasting using deep feature extraction and LSTM[J].IET Renewable Power Generation,2019,13(12):2062-2069.
[6]ZHANG D Y,LUO Y M.Simulation of Mixed Attribute Feature Detection for Large Network Data Based on Rough Set[J].Computer Simulation,2021,38(1):460-463,485.
[7]WU F F.Feature Extraction of Cosmetic Packaging Symbol Elements Based on Big Data Clustering[J].China Surfactant Detergent & Cosmetics,2020,50(1):44-48.
[8]LIN Q Y.Big Data Multi-lable Attribute Classification Techno-logy in Cloud Service Envirinment[J].Microeletronics & Computer,2019,36(2):101-104.
[9]LI Y,HU J C.Outlier Detection Based on Neighborhood Gra-nule for Mixed Attribute Data[J].Journal of Chinese Computer Systems,2020,41(4):855-860.
[10]LIU W J,JI W X,ZHANG C Y.Big Data Modeling Analysis Method for Intelligent Production Maintenance[J].China Mechanical Engineering,2019,30(2):37-44.
[11]YANG P,SHEN H T,TAO P,et al.Parallel permutation entropy feature extraction method for time series data based on cloud platform[J].Electric Power Automation Equipment,2019,39(4):217-223.
[12]DONG J W,WU W B,HUANG J X,et al.State of the Art and Perspective of Agricultural Land Use Remote Sensing Information Extraction[J].Journal of Geo-Information Science,2020,22(4):772-783.
[13]ZHANG B,XIONG C B.Automatic Point Cloud RegistrationBased on Voxel Downsampling and Key Point Extraction[J].Laser & Optoelectronics Progress,2020,57(4):109-117.
[14]WANG Y,LI J,ZENG H,et al.Real-time Feature ExtractionAlgorithm Based on Mutual Information[J].Journal of Chinese Computer Systems,2019,40(6):1242-1247.
[15]MADDUMALA V,ARUNKUMAR R.Big Data-Driven Feature Extraction and Clustering Based on Statistical Methods[J].Traitement du Signal,2020,37(3):387-394.
[16]ZHAO B,LI S,GAO Y,et al.A Framework of CombiningShort-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition[J].Sensors,2020,20(23):6984.
[17]LI Y,CHEN X,YU J.Feature extraction and classification ofship radiated noise based on VMD and SVM[J].Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology,2019,41(1):89-94.
[18]HEITMANN A J,GARDINER-GARDEN R S.A Robust Feature Extraction and Parameterized Fitting Algorithm for Bottom-Side Oblique and Vertical Incidence Ionograms[J].Radio Science,2019,54(1/2):115-134.
[19]LIANG L,DING X,LIU F,et al.Feature Extraction UsingSparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis[J].Sensors,2021,21(11):3680.
[20]NURMAINI S,TUTUKO B,RACHMATULLAH M N,et al.Machine Learning Techniques with LowDimensional Feature Extraction for Improving the Generalizability of Cardiac Arrhythmia[J].IAENG International Journal of Computer Science,2021,48(2):369-378.
[21]GOVINDARAJAN S,RAGAVAN V,EL-HAG A,et al.Deve-lopment of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification[J].Energies,2021,14(6):1564.
[22]LV Z,LLORET J,SONG H.Guest Editorial Software Defined Internet of Vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2021,22(6):3504-3510.
[1] HUANG De-cai and QIAN Chao-kai. Mixed Data Affinity Propagation Clustering Algorithm Based on Dimensional Attribute Distance [J]. Computer Science, 2015, 42(Z11): 55-57.
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