Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6): 66-73.doi: 10.11896/jsjkx.191000072

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

Big Data Decomposition-Fusion and Its Intelligent Acquisition

LIU Ji-qin1, SHI Kai-quan2   

  1. 1 School of Mathematics and Quantitative Economics,Shandong University of Finance and Economics,Jinan 250014,China
    2 School of Mathematics,Shandong University,Jinan 250100,China
  • Received:2019-10-12 Online:2020-06-15 Published:2020-06-10
  • About author:IU Ji-qin,born in 1968,Ph.D,professor.Her main research interests include big data intelligent analysis and application and rough system theory and application.
    SHI Kai-quan,born in 1945,professor,Ph.D supervisor.His main research interests include big data theory and application,information intelligent system.He proposed S-rough sets,function S-rough sets,P-sets,inverse P-sets,function P-sets and function inverse P-sets.
  • Supported by:
    This work was supported by the National Social Science Foundation of China (71663010) and Natural Science Foundation of Shandong Province, China (zr2013aq019)

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Abstract: The concepts of big data decomposition-fusion and big data distance generated by big data decomposition and fusion are given.By using these concepts,an union-intersection decomposition theorem of big data,an intersection-union decomposition theorem of big data and their attribute conjunction relation are given.Intelligent generation theorems and the distance relationship of big data fusion are given.A recognition criterion of big data decomposition-fusion,an intelligent algorithm and algorithm process of big data decomposition-fusion acquisition are given.The application of these theoretical results in big data decomposition-fusion intelligent acquisition is presented.In this paper,the new characteristics of ∧-type big data are given,∧-type big data is obtained by using P-sets model.

Key words: Big data, Decomposition-Fusion, Intelligent algorithm, Recognition criterion

CLC Number: 

  • O144
[1]SHI K Q.Big data structure-logic characteristics and big data law [J].Journal of Shandong University (Natural Science),2019,54(2):1-29.
[2]WANG F Y,CARLEY K M,ZENG D,et al.Social computing:from social informatics to social intelligence [J].IEEE Intelligent systems,2007,22(2):79-83.
[3]ZHANG N,WANG F Y,ZHU F,et al.DynaCAS:Computational experiments and decision support for ITS [J].IEEE Intelligent Systems,2008,23(6):19-23.
[4]WANG F Y.Parallel control and management for intelligent transportation systems:Concepts,architectures,and applications[J].IEEE Transactions on Intelligent Transportation Systems,2010,11(3):630-638.
[5]WANG F Y.Scanning the issue and beyond:Parallel driving with software vehicular robots for safety and smartness [J].IEEE Transactions on Intelligent Transportation Systems,2014,15(4):1381-1387.
[6]LV Y,DUAN Y,KANG W,et al.Traffic flow prediction with big data:A deep learning approach [J].IEEE Trans.Intelligent Transportation Systems,2015,16(2):865-873.
[7]LV Y,ZHANG X,KANG W,et al.Managing emergency traffic evacuation with a partially random destination allocation strategy:a computational-experiment-based optimization approach[J].IEEE Trans.Intelligent Transportation Systems,2015,16(4):2182-2191.
[8]YUAN Y,WANG F Y,ZENG D.Developing a cooperative bidding framework for sponsored search markets-An evolutionary perspective [J].Information Sciences,2016,369:674-689.
[9]WANG F Y.The emergence of intelligent enterprises:From CPS to CPSS [J].IEEE Intelligent Systems,2010,25(4):85-88.
[10]GOODHOPE K,KOSHY J,KREPS J,et al.Building LinkedIn’s real-time activity data pipeline [J].IEEE Data Eng.Bull.,2012,35(2):33-45.
[11]MCKUSICK K,QUINLAN S.GFS:evolution on fastforward [J].Communications of the ACM,2010,53(3):42-49.
[12]COOPER B F,RAMAKRISHNAN R,SRIVASTAVA U,et al.PNUTS:Yahoo!’s hosted data serving platform [J].Procee-dings of the VLDB Endowment,2008,1(2):1277-1288.
[13]MELNIK S,GUBAREV A,LONG J J,et al.Dremel:interactive analysis of web-scale datasets [J].Proceedings of the VLDB Endowment,2010,3(1／2):330-339.
[14]BU Y,HOWE B,BALAZINSKA M,et al.HaLoop:efficient iterative data processing on large clusters [J].Proceedings of the VLDB Endowment,2010,3(1／2):285-296.
[15]LAM W,LIU L,PRASAD S T S,et al.Muppet:MapReducestyle processing of fast data [J].Proceedings of the VLDB Endowment,2012,5(12):1814-1825.
[16]CHEN S.CHEETAH:a high performance,custom data warehouse on top of MapReduce [J].Proceedings of the VLDB Endowment,2010,3(1／2):1459-1468.
[17]CANIM M,MIHAILA G A,BHATTACHARJEE B,et al.SSD bufferpool extensions for database systems [J].Proceedings of the VLDB Endowment,2010,3(1／2):1435-1446.
[18]LUO T,LEE R,MESNIER M,et al.Storage-DB:heterogeneity-aware data management to exploit the full capability of hybrid storage systems [J].Proceedings of the VLDB Endowment,2012,5(10):1076-1087.
[19]WONG P C,SHEN H W,JOHNSON C R,et al.The top 10 challenges in extreme-scale visual analytics[J].IEEE computer graphics and applications,2012,32(4):63-67.
[20]PIKE R,DORWARD S,GRIESEMER R,et al.Interpreting the data:parallel analysis with Sawzall [J].Scientific Programming,2005,13(4):277-298.
[21]SHI K Q.P-sets [J].Journal of Shandong University (Natural Science),2008,43(11):77-84.
[22]SHI K Q.P-sets and its applications [J].Advances in Systems Science and Applications,2009,9(2):209-219.
[23]SHI K Q.P-sets and its applied characteristics [J].Computer Science,2010,37(8):1-8.
[24]SHI K Q.P-reasoning and P-reasoning discoveryidentification of information [J].Computer Science,2011,38(7):1-9.
[25]SHI K Q.P-sets,inverse P-sets and the intelligent fusionfilter identification of information [J].Computer Science,2012,39(4):1-13.
[26]FAN C X,LIN H K.P-sets and the reasoningidentification of disaster information [J].International Journal of Convergence Information Technology,2012,7(1):337-345.
[27]LIN H K,FAN C X.The dual form of P-reasoning and identification of unknown attribute [J].International Journal of Digital Content Technology and its Applications,2012,6(1):121-131.
[28]ZHANG L,CUI Y Q,SHI K Q.Outer P-sets and data internal recovery [J].Systems Engineering and Electronics,2010,32(6):1233-1238.
[29]WANG Y,GENG H Q,SHI K Q.P-sets and dependencediscovery of dynamic information [J].Systems Engineering and Electronics,2011,33(9):2035-2038.
[30]LI Y Y,LIN H K,SHI K Q.Characteristics of data discrete interval and data discovery-application [J].Systems Engineering and Electronics,2011,33(10):2258-2262.
[31]ZHANG L,TANG J H,SHI K Q.The fusion of internal packet information and its feature of attribute conjunction [J].Journal of Shandong University(Natural Science),2014,49(2):93-97.
[32]SHI K Q,ZHANG L.Internal P-set and data outerrecovery[J].Journal of Shandong University (Natural Science),2009,44(4):8-14.
[33]ZHANG G Y,ZHOU H Y,SHI K Q.P-sets and the recoveryidentification double [J].Systems Engineering and Electronics,2010,32(9):1919-1924.
[34]ZHANG L,REN X F.P-sets and (f,f)-heredity [J].Quantitative Logic and Soft Computing,2010,2(1):735-743.
[35]ZHANG L,XIU M,SHI K Q.P-sets and application of internal-outer data circle [J].Quantitative Logic and Soft Computing,2010,2(1):581-591.
[36]QIU Y F,CHEN B H.f-model generated by P-sets [J].Quantitative Logic and Soft Computing,2010,2(1):613-620.
[37]LI Y Y,ZHANG L,SHI K Q.Generation and recovery of compressed data and redundant data[J].Quantitative Logic and Soft Computing,2010,2(1):661-671.
[38]XIU M,SHI K Q,ZHANG L.P-sets and F-data selection-discovery [J].Quantitative Logic and Soft Computing,2010,2(1):791-799.
[39]ZHAO S L,FAN C X,SHI K Q.Outer P-information generation and its reasoning-searching discovery[J].Journal of Shandong University(Natural Science),2012,47(1):99-104.
[40]ZHAO S L,WU S L,SHI K Q.Internal Preasoning information recovery and attribute hiding searching discovery [J].Computer Science,2013,40(4):209-213.
[41]SHI K Q.Function P-sets[J].Journal of Shandong University (Natural Science),2011,46(2):62-69.
[42]SHI K Q.Function P-sets[J].International Journal of Machine Learning and Cybernetics,2011,2(4):281-288.
[43]SHI K Q.P-information law intelligent fusion and soft information image intelligent generation[J].Journal of Shandong University(Natural Science),2014,49(4):1-17.
[44]TANG J H,ZHANG L,SHI K Q.Intelligent fusion of information law and its inner separation[J].Computer Science,2015,42(2):204-209.
[45]LIN R,FAN C X.Function P-sets and dynamic characteristics of information law[J].Journal of Shandong University (Natural Science),2012,47(1):121-126.
[46]REN X F,ZHANG L,SHI K Q.Two types of dynamic information law models and their applications in information camouflage and risk identification[J].Computer Science,2018,45(9):230-236.
[47]CHEN B H,ZHANG L,SHI K Q.Intelligent dynamic fusion of packet information and the intelligent state recognition of information law[J].Journal of Shandong University (Natural Science),2018,53(2):83-87.
[48]TANG J H,ZHANG L,SHI K Q,et al.Outer Pinformation law reasoning andits application in intelligent fusion and separating of information law[J].Microsystem Technologies,2018,24(10):4389-4398.
[49]SHI K Q.P-augmented matrix and dynamic intelligent discovery-identification of information[J].Journal of Shandong University (Natural Science),2015,50(10):1-12.
[50]LIN H K,FAN C X.Embedding camouflage of inverse P-information and application[J].International Journal of Convergence Information and Technology,2012,7(20):471-480.
[51]FAN C X,HUANG S L.Inverse P-reasoning discovery identification of inverse P-information[J].International Journal of Di-gital Content Technology and its Applications,2012,6(20):735-744.
[52]SHI K Q.Function inverse P-sets and the hiding information generated by function inverse P-information law fusion[C]// Proceedings of the 13th IFIP WG 6.11 Conference on e-Business,e-Services,and e-Society,Sanya,China,2014:224-237.
[53]GUO H L,REN X F,ZHANG L.Relationships between dynamic data mining and P-augmented matrix[J].Journal of Shandong University (Natural Science),2016,51(8):105-110.
[54]GUO H L,CHEN B H,TANG J H.Inverse P-sets and intelligent fusion mining-discovery of information[J].Journal of Shandong University (Natural Science),2013,48(8):97-103,110.
[55]ZHANG L,REN X F,SHI K Q.The dynamic segmentation characteristics of P-augmented matrix and the dynamic intelligent acquisition of P-information[J].International Journal of Applied Decision Sciences,2016,9(4):413-425.
[56]ZHANG L,REN X F,SHI K Q,et al.Inverse packet matrix reasoning model-based the intelligent dynamic separation and acquisition of educational information[J].Microsystem Technologies,2018,24(10):4415-4421.
[57]REN X F,ZHANG L,SHI K Q,et al.Inverse P-augmented matrix method-based the dynamic findings of unknown information[J].Microsystem Technologies,2018,24(10):4187-4192.
[58]ZHANG L,REN X F,SHI K Q.Inverse P-information law models and the reality-camouflage intelligent transformations of information image[C]//Proceedings of the 2016 International Conference on Network and Information Systems for Compu-ters,Washington:IEEE,2016:337-341.
[59]ZHANG L,REN X F,SHI K Q.Inverse P-data models and data intelligent separation[C]//Proceedings of the 2016 International Conference on Electronic Information Technology and Intellectualization,DOI:10.12783/dtcse/iceiti2016/6107,2016.
[60]REN X F,ZHANG L,SHI K Q.Surplusdeficiency of cardinal number and inverse P-augmented matrices[J].Journal of Shandong University (Natural Science),2015,50(10):13-18,26.
[61]ZHANG L,REN X F.Surplus-deficient theorem of cardinal number and data internal-outer miningseparation [J].Journal of Shandong University (Natural Science),2015,50(8):90-94.
[62]GUO H L,ZHANG L.Data separation and its attribute state characteristics [J].Journal of Shandong University (Natural Science),2017,52(12):89-94.
[63]TANG J H,CHEN B H.Intelligent fusion of internal inverse packet information and expansion relationship of attribute disjunction[J].Journal of Shandong University (Natural Science),2014,49(2):89-92,97.
[64]ZHANG L,REN X F,SHI K Q.Intelligent switchcamouflage of information laws and P-law augmented matrices [J].Journal of Shandong University (Natural Science),2016,51(8):90-97.
[65]REN X F,ZHANG L,SHI K Q.Boundary Characteristics of Inverse P-sets and System Condition Monitoring[J].Computer Science,2016,43(10):211-213,255.
[66]REN X F,ZHANG L.Perturbation theorems of inverse P-sets and perturbation-based data mining[J].Journal of Shandong University (Natural Science),2016,51(12):54-60.
[67]LIU J Q,ZHANG H Y.Information P-dependence and P-dependence mining-sieving [J].Computer Science,2018,45(7):202-206.
[68]SHI K Q,YAO B X.Function S-rough sets and law identification[J].Science in China E:Information Science,2008,38(4):553-564.
[69]SHI K Q,ZHAO J L.Function S-rough sets and security-authentication of hiding law[J].Science in China E:Information Science,2008,38(8):1234-1243.
[70]SHI K Q,YAO B X.Function S-rough sets and law identification[J].Science in China F:Information Science,2008,51(5):499-510.
[71]SHI K Q,ZHAO J L.Function S-rough sets and security-authentication of hiding law[J].Science in China F:information Sciences,2008,51(7):924-935.
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