Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230300109-8.doi: 10.11896/jsjkx.230300109

• Big Data & Data Science • Previous Articles     Next Articles

Study on Value Calculation of Big Data Based on Granular Tree and Usage Relationship

MA Wensheng1, HOU Xilin2, WANG Hongbo2, LIU Sen2   

  1. 1 School of Electtronic and Information Engineering,Liaoning University of Science and Technology,Anshan,Liaoning 114051,China
    2 School of Business Administration,Liaoning University of Science and Technology,Anshan,Liaoning 114051,China
  • Published:2023-11-09
  • About author:MA Wensheng,born in 1971,Ph.D candidate.His main research interest is big data application.
    HOU Xilin,born in 1960,Ph.D,professor.His main research interest include big data application,enterprise innovation system.

Abstract: Study the core “data results of value” of big data.Firstly,the rough set method,cluster-based method,quotient space method,fuzzy information method and cloud model method for granulating big data are described.According to their common characteristics —“division”,the big data is “granulated”,and a “granularity tree” is established in the big data according to the size of division.“granular space” is defined in the “granular Tree”.Then it defines the usage relationship between the granular space and the representative project,and the conditions that the usage relationship of different granular spaces meets.Finally,according to the usage of each particle and each particle set in the usage relationship of the particle space,the usage is divided into three types:“regular use” “inevitable use”and “related use”.Take the average value of their attributes and objects and round them to 0~100,as the values of “data results of value”“inevitable value” and “relevant value” of big data.The effective calculation method of the core “data results of value” of big data is given,and the application examples of the core “data results of value” calculation of big data in telemedicine,urban management,universities and other fields are also given.

Key words: Big data value, Data results of value, Granularity tree, Usage relationship, Regular use, Inevitable use, Related use

CLC Number: 

  • TP311
[1]TOFFLER A.The third wave[M].New York:Bantam Books,1981:167-168.
[2]MAYER-SCHÖNBERGER V,CUKIER K.Big data:a revolution that will transform how we live,work,and think [M].New York:Houghton Mifflin Harcourt,2013:7,47.
[3]BORGATTI S P,MEHRA A,BRASS D J,et al.Net-work analysis in the social sciences[J].Science,2009,323(5916):892-895.
[4]PORTER M E.Competitive advantage:creating and sustaining superior performanc[M].New York:Free Press,1985:2-4.
[5]XU Z B,FENG Z Y,GUO X H,et al.Frontier issues of management and decision making driven by Big data[J].Management World,2014(11):158-163.
[6]MAYER-SCHÖNBERGER V,CUKIER K.Bigdata:a revolu-tion that will transform how we live,work,and think[M].Hought on Mifflin Harcourt,2013.
[7]WANG N,LI T Z,CAO S Y,et al.Research on the formation path of big data value:a biological analogy [J].China Science and Technology Forum,2020,294(10):142-149.
[8]MA W S,HOU X L,WANG H B,et al.Big data value calculation method based on granular computing and usage times [J].Journal of Liaoning University of Science and Technology,2021,44(3):196-207.
[9]Global big data exchange[OL].https://www.gzdex.com.cn/.
[10]Donghu Big Data[OL].http://www.chinadatatrading.com/.
[11]Beijing International Data Exchange[OL].https://www.bji-dex.com/.
[12]Dawex:Sell,buy and share data[OL].https://www.dawex.-com/en/.
[13]Xignite[OL].https://www.xignite.com/.
[14]World Quant[OL].https://www.worldquant.com/data-ex-change/.
[15]JIANG D,YUAN Y,ZHANG X W,et al.Summary of data pricing and trading research [J/OL].Journal of Software:1-29.[2013-03-04].http://221.203.21.203:8001/rwt/CNKI/https/MSYXTLUQPJUB/10.13328/j.cnki.jos.006751.
[16]FAMA E F,FRENCH K R.The value premium and the CAPM[J].Journal of Finance,2006,61(5):2163-2185.
[17]HANSEN L P,SARGENT T J.Formulating and estimating dynamic linear rational expectations models[J].Australian Journal of Otolaryngology,1980,2(1):7-46.
[18]SHILLER R J.Theuse of volatility measures in assessing market efficiency[J].Journal of Finance,1981,36(2):291-304.
[19]DAVID T.Valuing intellectual property assets[J].Computer and Internet Lawyer,2002,19(2):1-8.
[20]CHIU Y J,CHEN Y W.Using AHP in patent valuation[J].Mathematical and Computer Modelling,2007,46(7/8):1054-1062.
[21]CHEN Z Z,WANG H Z,XIONG F,et al.Pricing strategy and method of big data auction [J].Journal of China University of Science and Technology,2018,48(6):486-494.
[22]LIN G T R,TANG J Y H.Appraising intangible assets from the viewpoint of value drivers[J].Journal of Business Ethics,2009,88(4):679-689.
[23]JORGE M,ISMAEL C,BIBIANO R,et al.A data quality in use model for big data[J].Future Generation Computer Systems,2016,63:123-130.
[24]NIYATO D,ALSHEIKH M A,WANG P,et al.Market model and optimal pricing scheme of big data and internet of things(IoT)[C]// IEEE International Conference on Communications(ICC).IEEE,Kuala Lumpur,2016.
[25]HOU X,SHEN J.Construction and analysis of big data value model in relay innovation[J].Journal of University of Science and Technology Liaoning,2019,42(2):149-153,160.
[26]VANCE A.Start-up goes after big data with Hadoophelper[OL].http://bits.blogs.nytimes.com/2010/04/22/start-up-goes-after-big-data-with-hadoophelper/?dbk.
[27]CHEN C L P,ZHANG C Y.Data-intensive applications,challenges,techniques and technologies:Asurvey on bigdata[J].Information Sciences,2014,275:314-347.
[28]XU J,WANG G Y,YU H.Review of Big Data Processing Based on Granular Computing[J].Chinese Journal of Computers,2015,38(8):1497-1517.
[29]YAO Y Y.Information granulation and rough set approximation[J].International Journal of Intelligent Systems,2001,16(1):87-104.
[30]HUA Q Y.Granulation Mechanism and Data Modeling forComplex Data[D].Taiyuan:School of Computer and Information Technology,Shanxi University,2011.
[31]ZHANG Y P,ZHANG L,WU T.The Representation of Diffe-rent Granular Worlds:A Quotient Space[J].Chinese Journal of Computers,2004,27(3):328-333.
[32]ZADEHL A.Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J].Fuzzy Sets and Systems,1997,90:111-127.
[33]MA H Y,WANG G Y,ZHANG Q H,et al.Multi-granularity color image segmentation based on cloud model[J].Computer Engineering,2012,38(20):184-187.
[34]QIN K,LI D Y,XU K.Imagesegmentationbasedon cloudmodel[J].Journal of Geomatics,2006,31(5):3-5.
[35]LIU C Y,FENG M,DAI X J,LI D Y.A New Algorithm of Backward Cloud[J].Journal of System Simulation,2004,16(11):2417-2420.
[36]ZHANG L,ZHANG B.Theory of fuzzy quotient space(methods of fuzzy granular computing)[J].Journal of Software,2003,14(4):770-776.
[37]LIN F,COHEN W W.Power iteration clustering[C]//Procee-dings of 2010 International Conference on Machine Learning(ICML).Haifa,ISRAEL,2010:655-662.
[38]YAN W,BRAHMAKSHATRIYA U,XUE Y,et al.p-PIC:Pa-rallel power iteration clustering for big data[J].Journal of Parallel and Distributed Computing,2013,73(3):352-359.
[39]WANG G Y,LI D Y,YAO Y Y,et al.Cloud Model and Granular Computing[M].Beijing:Science Press,2012.
[1] ZENG Wang-lin, SHE Yan-hong. Object-oriented Multigranulation Formal Concept Analysis [J]. Computer Science, 2018, 45(10): 51-53.
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