计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 230300109-8.doi: 10.11896/jsjkx.230300109

• 大数据&数据科学 • 上一篇    下一篇

基于粒度树和使用关系的大数据价值计算研究

马文胜1, 侯锡林2, 王宏波2, 柳森2   

  1. 1 辽宁科技大学电子与信息工程学院 辽宁 鞍山 114051
    2 辽宁科技大学工商管理学院 辽宁 鞍山 114051
  • 发布日期:2023-11-09
  • 通讯作者: 侯锡林(hou.xilin@163.com)
  • 作者简介:(1391291002@qq.com)

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.

摘要: 文中研究了大数据最基本的核心“价值数值”。首先阐述了对大数据进行粒化的粗糙集方法、基于聚类的方法、商空间法、模糊信息方法和云模型方法等,并按它们的共同特性——“划分”,对大数据进行“粒化”,按划分的粗细在大数据中建立了“粒度树”,在“粒度树”中定义了“粒空间”。然后定义了粒空间与代表项目之间的使用关系,以及不同粒空间的使用关系满足的条件。最后按照在粒空间的使用关系中每个粒及每个粒集合的使用情况,将使用情况分为3种:“正则使用”“必然使用”“相关使用”。取它们的属性及对象的平均值,并圆整到0至100,作为大数据的“正则价值”“必然价值”“相关价值”的数值。给出大数据最基本的核心“价值数值”的有效计算方法,又给出大数据最基本的核心“价值数值”计算在远程医疗、城市管理、高等院校等多个领域的应用实例。

关键词: 大数据价值, 价值数值, 粒度树, 使用关系, 正则使用, 必然使用, 相关使用

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

中图分类号: 

  • 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.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!