《6G知識定義的編排與管控白皮書》（英文）

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Abstract

Withincreasinglycomplexmobilecommunicationnetworksanddifferentiatedservicerequirementsofawidevarietyofindustries,themostimportantresearchdirectionandtrendforbeyond5G(B5G)/6GistodeeplyintegrateAI,bigdata,andcomputingintonetworks,anddevelopintelligentcommunicationnetworkstodeliverautonomousdrivingnetworkcapabilitiesandpreciselycustomizedservices.Embeddingdataandintelligenceintonetworksinordertodevelopanovelnetworkintelligencetechnologysystemfeaturingendogenousintelligenceaswellascomputeandcommunicationconvergenceraiseschallengesintermsofarchitecture,data,andAIalgorithms.Data,asthemostfundamentalandimportantpart,determinestheupperlimitofnetworkintelligenceperformance,whereasthearchitectureandalgorithmsarejustthemeanstoapproachthislimit.Therefore,weneedtoaddressthemostfundamentalissueofhowtoacquire,analyze,andleveragemobilebigdataandbuildacomprehensivedataacquisitionandanalysissysteminordertofuelthedevelopmentofthenovelB5G/6Gnetworkintelligencetechnologysystem.ThiswhitepaperpresentsadataacquisitionandanalysissystemofB5G/6Gnetworkintelligence.Thesystemrealizesdata-levelinformationintegration,information-levelknowledgeextractionandrepresentation,intelligence-levelknowledgecomputingandinference,andapplication-levelfeaturecustomization.Thesefunctionsareofferedthroughmobilenetworkdataacquisitionandstorage,translationofmobilecommunicationprinciplesandprotocols,networkdataknowledgegraphconstructionandknowledgegraph-basedparsing,andconstructionandevaluationoffeaturedatasetsfornetworkintelligence.Thepaperseekstocreateafeasibleapproachtodataacquisitionandanalysisofnetworkintelligence,exploringanoveldirectionandparadigmfornetworkintelligencedatatechnologies.

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Contents

1VisionandChallengesofB5G/6GNetworkIntelligence 6

2DataAcquisitionandAnalysisSystemofB5G/6GNetworkIntelligence 9

2.1WirelessNetworkIntelligentOpenPlatform 9

2.2DataAcquisition 10

2.3KnowledgeGraph-basedRepresentationandAnalysis 11

2.4FeatureDataSetsConstruction 12

3ConstructionandApplicationsofNetworkDataKnowledgeGraphs 13

3.1IntelligentFeatureEngineeringofMobileBigData 13

3.2ConstructionofNetworkDataKnowledgeGraphs 14

3.2.1TechnicalArchitectureofKnowledgeGraphs 14

3.2.2ConstructionofNetworkDataKnowledgeGraphsBasedonCommunicationPrinciplesand

Protocols 15

3.2.3KnowledgeGraphCompletionMethod 15

3.2.4EdgeWeightsofKnowledgeGraphs 16

3.2.5NodeImportanceandEfficiencyofImpactBetweenNodesinKnowledgeGraphs 16

3.3ApplicationsofNetworkDataKnowledgeGraphs 16

4ConstructionandEvaluationofFeatureDataSetsforNetworkIntelligence 19

4.1DataModelsforNetworkDataKPIs 19

4.2PreliminaryKnowledgeGraph-basedFilteringofFeaturesThatAffectKPIsinSpecificScenarios

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4.3ConstructionofFeatureDataSetsBasedonMachineLearning 20

4.4EvaluationSystemfortheConstructionProcessofFeatureDataSets 20

4.5EvaluationSystemforKPI-orientedFeatureDataSets 21

5FeatureDataSetConstructionandEvaluationCases 22

5.1ConstructionofaLocalKnowledgeGraphwiththeUplinkThroughputsatItsCore 22

5.2In-depthAnalysisoftheLocalKnowledgeGraphwiththeUplinkThroughputsatItsCore 23

5.3ConstructionoftheUplinkThroughputFeatureDataSet 24

5.4EvaluationoftheUplinkThroughputFeatureDataSet 25

6Outlook 26

References 27

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Preface

Withincreasinglycomplexmobilecommunicationnetworksanddifferentiatedservicerequirementsofawidevarietyofindustries,themostimportantresearchdirectionandtrendforbeyond5G(B5G)/6GistodeeplyintegrateAI,bigdata,andcomputingintonetworks,anddevelopintelligentcommunicationnetworkstodeliverautonomousdrivingnetworkcapabilitiesandpreciselycustomizedservices.Atpresent,globalresearchonthisdirectionisstillinitsinitialstage.Embeddingdataandintelligenceintonetworksinordertodevelopanovelnetworkintelligencetechnologysystemfeaturingendogenousintelligenceaswellascomputeandcommunicationconvergenceraiseschallengesintermsofarchitecture,data,andAIalgorithms.Data,asthemostfundamentalandimportantpart,determinestheupperlimitofnetworkintelligenceperformance,whereasthearchitectureandalgorithmsarejustthemeanstoapproachthislimit.Whenmobilecommunicationnetworksarerunning,tensofthousandsofdatafieldsandindicatorsaregeneratedthroughoutthenetworks,fromUEs,radioaccessnetworks(RANs),transmissionbearernetworks,tocorenetworks,involvingdifferentsoftware,hardware,functions,andprotocolstacks.Forthedevelopmentofnetworkintelligence,itisimperativetoestablishacomprehensivenetworkdataacquisitionandanalysissystemthateffectivelyacquires,classifies,analyzes,andleveragesdifferenttypesofdata.

Asnetworkstructures,UEtypes,UEbehaviors,dataservicerequirements,andsystemresourceconfigurationsofB5G/6Gwillbehighlydynamic,time-sensitive,andcoupled,dataacquisitionandanalysisonmobilenetworkswillfacemanychallenges.Amajordifficultyliesindataacquisitionbecausedataisscatteredondifferentdevicesatdifferentlayers.Alongwiththis,variousdatatypesandcomplexstructuresmakeitdifficulttoanalyzedata,andhighlyabstractdatahindersunderstanding.Beyondthis,coupleddatawithcomplexcorrelationsresultsindifficultiesinmining.Tocopewiththesechallengesrequiresstandarddataattributegeneralization,efficientlyclarifyingrelationshipsbetweendata,furtherexploringin-depthassociationsbetweenrelationships,andclearlyrepresentingdataandkeyinformationinherentinrelationships.

Networkintelligencereliesheavilyondata.Establishingacompletedataacquisitionandanalysissystemthatsolvesthebasicissuesofdataacquisition,openness,andutilizationwilladvancethedevelopmentofnovelB5G/6Gnetworkintelligencetechnologiesandbecomeakeymilestonetowardsrealizingnetworkintelligence.Thispaperattemptstocreateafeasibleapproachtodataacquisitionandanalysisofnetworkintelligence,seekinganoveldirectionandparadigmfornetworkintelligencedatatechnologies.Knowledgegraph,asanewmethodofnetworkdataknowledgerepresentationandanewtoolofknowledgemanagement,clarifiescomplexcorrelationsbetweendata,andrepresentstypesandattributesofdataaswellasthoseofrelationshipsbetweendata.Furthertothis,topologyandnodeinformationofgraphsenablesintelligentinferenceandintelligentfeatureengineeringpractices.Knowledgegraphisefficientintacklingtheaforementionedchallengesindataacquisitionandanalysisonmobilecommunicationnetworks.ItiskeytothedataacquisitionandanalysissystemofB5G/6Gnetworkintelligence.

ThiswhitepaperpresentsadataacquisitionandanalysissystemofB5G/6Gnetworkintelligence.Theachievementsofthissystemaremadeinmobilenetworkdataacquisitionandstorage,knowledgerepresentation,knowledgeinference,andconstructionandevaluationoffeaturedatasets.

KnowledgerepresentationofB5G/6Gmobilecommunicationnetworksbyusingtoolssuchasknowledgegraph,changesthenatureofcomplexinternalrelationshipsofmobilecommunicationnetworksfrom"blackbox"to"whitebox."Itpresentshighlydynamic,time-sensitivemobilecommunicationnetworkswitheasilycoupledelementsinavisualized,hierarchical,andstructuredmannerbasedonmobilecommunicationprinciplesandprotocols.Onthebasisofknowledgerepresentation,knowledgeinferenceofB5G/6Gmobilecommunicationnetworkseffectivelyprocessesandintegratesmassivedataandcomplexconnectionsonnetworksforquickknowledgeresponseandinference.Knowledgeinferenceempowersmobilecommunicationnetworkswithperceptionandcognitioncapabilities.

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Basedonthecorrelationanalysisbetweendatafieldsinknowledgegraphsofmobilecommunicationnetworksandservicerequirements,dataisfurtherclassifiedintodifferenttypesandfeatureextractionisperformedonthedatatoconstructfeaturedatasetsofB5G/6Gmobilecommunicationnetworks.Featuredatasetsrepresentkeyfeaturesthathavesignificantimpactonkeyperformanceindicators(KPIs)ofmobilecommunicationnetworks.Thesetsalsocontainthedatacollectedbyfeatures.Asacombinationofexpertknowledgeandintelligentalgorithms,thesetscanserveasdirectinputsforsubsequentintelligentoptimization.Constructingsuchfeaturedatasetshelpsobtainimportantfeaturescloselyrelatedtotargetindicatorsduringintelligentdiagnosis,locating,andoptimizationofmobilecommunicationnetworks.Thisguidesfurtherintelligentnetworkperception,prediction,optimization,andeventually,digitaltwins.

Organizations:PurpleMountainLaboratoriesforNetworkCommunicationandSecurity,SoutheastUniversity,BeijingUniversityofPostsandTelecommunications,CICTMobileCommunicationTechnologyCo.,Ltd.,HuaweiTechnologiesCo.,Ltd.,Ericsson(China)CommunicationsCo.,Ltd.,vivoMobileCommunicationCo.,Ltd.,andInstituteofChinaTelecomCorporationLimited

Contributors:XiaohuYou,YongmingHuang,HangZhan,ShiwenHe,YunshanYi,JianjieYou,WenjingLi,LeiFeng,HuiXu,WanfeiSun,MingyuZhao,XueqiangYan,LileiWang,RenqianZhao,YannanYuan,BingQian,LuGao,andYangLiu

1VisionandChallengesofB5G/6GNetworkIntelligence

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Inthepursuitofbeyond5G/6G(hereinafterreferredtoasB5G/6G),mobilecommunicationnetworkswillempowerawholerangeofindustriesandthereforefacechallengesposedbydiversifieddemandsinnumerousapplicationscenarios.ThetrendforB5G/6GistodeeplyintegrateAIandbigdataintocommunicationfornetworkintelligence.Incontrastwith4Gnetworksthatfocusonmobilephonecommunication,5GandB5Gneedtosupportall-scenariofullservicesacrossindustries,promoteintelligentdigitaltransformationofawidevarietyofindustries,anddelivernewhigh-valueservicesandtheultimateserviceexperience.Tomeettheserequirements,inadditiontointegratingtraditionalterrestrialnetworks,6Gwirelesscommunicationnetworkswillintegratesatellitecommunication,unmannedaerialvehicle(UAV)communication,andothernon-terrestrialnetworksforspace-air-ground-seaintegratedaccess.Allspectrawillbefullyexploredtofurtherimprovedatatransmissionratesandconnectiondensity.Additionally,6GwilladoptAIandbigdatatechnologiestointroducearangeofsmartapplicationsinordertocopewithmassivedatageneratedbytheuseofextremelyheterogeneousnetworks,variouscommunicationscenarios,numerousantennas,highbandwidths,andnewservicerequirements.Clearly,6Gmobilenetworkswillbedeeplyintegratedwithcomputing,bigdata,andAItechnologiestoprovidefullapplicationservicesfrompersonalapplications,verticalapplications,togovernanceapplications.Itisexpectedthat6Gwilldelivernewparadigmsfeaturing"globalcoverage","allspectra","fullapplications"[1,2].

Future6Gnetworksareexpectedtomeetservicerequirementsforsupportingvirtualandphysicalfusion,holographic,scenario-based,personalized,andubiquitouscommunication,aswellasmeetnetworkrequirementsfortheintegrationofheterogeneousnetworktechnologiesandspace-air-ground-seaintegratednetworks.However,thecurrentnetworkoperationparadigmswithrule-basedalgorithmsarelimitedbyrigidpresetrules,anditisdifficulttodynamicallyadapttheparadigmstoever-changinguserrequirementsandnetworkenvironments.Moreover,networkoperationexperiencecannotbeeffectivelyaccumulated,whichlimitscontinuousimprovementofnetworkcapabilities.Thatistosay,networksdonothavetheself-evolutioncapabilityinthecurrentparadigms.Upgradesorimprovementsrequiretime-consumingresearchbyengineers,whichisunacceptablefor6Gnetworkswithunprecedentedscaleandoperationcomplexity.AddingendogenousintelligenceintonetworkstoimproveautomationandintelligenceofB5G/6Giskeytohandlingtheprecedingissues[1].Inparticular,dataisessentialforB5G/6Gnetworkintelligence.Onlybyleveragingmobilebigdata,canweoptimizenetworksintermsofserviceexperience,networkquality,networkefficiency,andnetworkcosts[4].

Majorchallengesaretoutilizemobilebigdataandembedintelligenceintonetworksinordertointroduceanovelnetworkintelligencetechnologysystemintermsofdata,architecture,andAIalgorithms.Toaddressthechallenges,acomprehensivenetworkdataacquisitionandanalysissystemisneeded.Basedondataacquisitionandanalysisresults,thesystemcanbettermeetdifferentiatedapplicationrequirements,enablingflexiblenetworkfunctionorchestration,morerefinedmobilecommunicationnetworkresourceallocation,andefficientnetworkcontrol.

Asnetworkstructures,UEtypes,UEbehaviors,dataservicerequirements,andsystemresourceconfigurationsofB5G/6Gwillbehighlydynamic,time-sensitive,andcoupled,acquisitionandanalysisofmobilebigdatawillfacemanychallenges.Amajordifficultyliesindataacquisitionbecausedataisscatteredondifferentdevicesatdifferentlayers.Alongwiththis,variousdatatypesandcomplexstructuresmakeitdifficulttoanalyzedata,andhighlyabstractdatahindersunderstanding.Beyondthis,coupleddatawithcomplexcorrelationsresultsindifficultiesinmining.Tocopewiththesechallengesrequiresstandarddataattributegeneralization,efficientlyclarifyingrelationshipsbetweendata,furtherexploringin-depthassociationsbetweenrelationships,andclearlyrepresentingdataandkeyinformationinherentinrelationships.Inaddition,tensofthousandsofdatafieldsandindicatorsaregeneratedthroughoutmobilecommunicationnetworks,fromUEs,accessnetworks,tocorenetworks,andinvolvedifferentsoftware,hardware,functions,andprotocolstacks.

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Forthedevelopmentofintelligenceinmobilecommunicationnetworks,itiskeytoeffectivelycollect,classify,analyze,andleveragedifferenttypesofgenerateddata,fullyunleashnetworkservicepotential,andgivefullplaytothetechnicaladvantagesofnetworks.

Traditionaldataanalysismethodscanbeclassifiedintotwotypes.Thefirsttypeismodelinganalysisbasedontheoriesandexperienceaccumulatedinthecommunicationfield.Forexample,wehaveexpertsystemsforrootcauseanalysisofanomalies,anomalydetectionbasedonmultipleKPIsinthewirelessnetworkknowledgebase[3],andoptimizationmodelanalysisforradioresourcemanagementoptimization.Thesecondtypeisdata-drivenmachinelearningmodelsandalgorithms,whichfocusonprovidingatypeofefficientalgorithmsforcertainproblems.AsB5G/6Gnetworksareexpectedtobemorecomplexthanever,anddata-drivenAItechnologieshavelimitations,theselearningmodelsandalgorithmscannotsupporttheend-to-endprocessfromthedetectionofnetworksymptomstotheimplementationofsolutions.Thisproblemiswidespreadinmobilecommunicationswhose"blackbox"internalmechanismlacksexplainabilityandscalability.Forthisreason,thesingleknowledge/model-drivenordata-drivenmodecannotmeettherequirementsofbigdataanalysisinB5G/6Gmobilecommunications.Dataanalysisinmobilecommunicationsneedstotransformintoanintelligentmodedrivenbybothknowledge/modelsanddata.

AsAIiseffectiveatdataanalysisinmobilecommunications,itsdevelopmentisofprimesignificance.EarlyformsofAI,representedbyexpertsystems,emphasizethelawofcausalityandknowledgestructurerelatedtosymbolicinferenceatthecostofviolatingthelawofexcludedmiddle.SubsequentformsofAI,representedbystatisticalmachinelearninganddeeplearning,valuethelawofexcludedmiddleatthecostofviolatingthelawofcausality.AIdevelopmentrequiresacombinationofthesetwotypesofAI.Attheendof2018,thetheoreticalframeworkofthethird-generationAIwasofficiallyreleased:(1)BuildingexplainableandrobustAItheoriesandmethods(2)Developingsecure,reliable,trustworthy,andscalableAItechnologies.TheAIroadmaphighlightsAItheoriesandmethodsofdataandknowledgeintegration.Asknowledgegraphisrelativelymatureatpresent,itcanfacilitateimplementationofthethird-generationAI.

Brain-likedatagovernancesystemshavebeenappliedtomanyfieldsbasedontoolslikeknowledgegraph,formingadata-information-knowledge-intelligencetechnicalpath.Suchclosed-loopbrain-likesystemsareatrade-offsolutionbetweenthetwotypesofAI.Likewise,inthemobilecommunicationsfield,toembeddataandintelligenceinordertointroduceanovelnetworkintelligencetechnologysystemalsoneedstofollowthedata-information-knowledge-intelligencepath.Therefore,suchatechnologysystemwillrelyondataacquisitionandanalysis.Knowledgegraph,criticaltothetechnicalpathofnetworkintelligence,integratestheadvantagesoffirst-andsecond-generationAI,andwillbekeyinpromotingB5G/6Gnetworkintelligence.

Knowledgegrapheffectivelyclarifiesthecorrelationsbetweenmulti-sourceheterogeneousmobilecommunicationdatawithloosedatastructures,hierarchicalandflattenednetworks,andendogenousnetworkperformanceelements.Inthismanner,knowledgegraphhelpsrealizeknowledgeinterconnectivity,efficientresourcemanagement,andintelligentnetworkmaintenance.Theendogenouselementsandcorrelationsofmobilecommunicationnetworkscanbedynamicallypresentedandaccuratelypositionedinaneasy-to-understandmannerwithclearstructures.Knowledgegraph-baseddataanalysisiscrucialtomobilecommunicationknowledgeacquisition,sorting,andrepresentation.

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Basedoncorrelationanalysisusingnetworkdataknowledgegraphs,featuresoftargetindicatorsarepreliminarilyfiltered.Aftercontinuousalgorithmoptimizationandmodeltraining,featuredatasetsfordifferentapplicationscenariosareconstructed.Thisentireprocessrealizesfrommobilecommunicationknowledgesortingtomobilecommunicationintelligenceapplication.

Figure1-1Bigdata-basedcognitiveintelligenceconstructioncommonlyusedinvariousindustries[13]

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2DataAcquisitionandAnalysisSystemofB5G/6GNetworkIntelligence

Embeddingdataandintelligenceintonetworksinordertodevelopanoveltechnologysystemraiseschallengesintermsofarchitecture,data,andAIalgorithms.Tocopewiththesechallenges,acomprehensivenetworkdataacquisitionandanalysissystemisneeded.Thesystemconsistsoffourfunctionalmodules:wirelessnetworkintelligentopenplatform,dataacquisition,knowledgegraph-basedcorrelationanalysis,andfeaturedatasetsconstruction.Thesemodulesenabledata-levelinformationintegration,information-levelknowledgeextraction,intelligence-levelknowledgecomputing,andapplication-levelfeaturecustomization.

Thewirelessnetworkintelligentopenplatformmoduleandthedataacquisitionmodulefocusonlayer-by-layerdataacquisitioninB5G/6G.Theknowledgegraph-basedcorrelationanalysismoduleandthefeaturedatasetsconstructionmodulehelpcopewithchallengingdatarepresentation,dataanalysis,andfeatureextractioninB5G/6G.ThefourmodulesworktogethertolayadatafoundationforB5G/6Gnetworkintelligenceandexplorewaystorealizeit.

Figure2-1DataacquisitionandanalysissystemofB5G/6Gnetworkintelligence

2.1WirelessNetworkIntelligentOpenPlatform

Thewirelessnetworkintelligentopenplatform,basedonB5G/6Gnetworksanddatawarehouses,deeplyintegratesbigdataandAI.GuidedbythenotionofthedataacquisitionandanalysissystemofB5G/6Gnetworkintelligence,theplatformaimsatdataopenness,datasharing,dataanalysis,anddataapplication.

TheB5G/6Gwirelessnetworkintelligentopenplatformmoduledeliversavisualizedone-stopsolutionforreal-timemobilenetworkdataacquisition,analysis,andtracing.Itprovidesprofessionalnetworkdatasupportandcontributestoapremiumecosysteminordertorealizerealdataopennessintheindustry.Thearchitectureofthisintelligentopenplatformconsistsofthreelayers:dataacquisitionandpreprocessinglayer,dataanalysislayer,anddataapplicationlayer,asshowninthefollowingfigure.

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Figure2-2B5G/6Gwirelessnetworkintelligentopenplatform

Theopenplatformaccessesmulti-dimensionaldiverseB5G/6GdatathroughAPIstoformtime-varying,highlyintegratedB5G/6Gdatawarehousesthatareorientedtoapplicationsandhaverelativelystablestructures.Basedonsuchdatawarehouses,anetworkdataacquisitionandanalysissystemisestablished,whichcoversthenetworkdataknowledgegraphconstruction,knowledgegraph-basedcorrelationanalysis,constructionoffeaturedatasets,and5Gnetworkbigdatadictionary.The5Gnetworkbigdatadictionaryisacompletedatasystemthatreflectsthecharacteristicsofmobilenetworkoperations.Itwilllayasolidfoundationforthedevelopmentofnetworkintelligence.Theoutputs(generatedfromthenetworkdataknowledgegraphconstruction,knowledgegraph-basedcorrelationanalysis,andfeaturedatasetconstruction)interactwiththe5Gnetworkbigdatadictionary.Inparticular,majorachievementsmadethroughinteractionwillbeincorporatedinthedictionary.Theseachievementsatthedataanalysislayerhelprealizeapplicationssuchasdigitaltwin,anomalydetection,sourcetracing,intelligentoptimization,intelligentdecision-making,andintelligentevaluation.

2.2DataAcquisition

ThedataacquisitionmoduleisafundamentalpartofthedataacquisitionandanalysissystemofB5G/6Gnetworkintelligence.Withoutthismodule,allsubsequentdataanalysisandintelligentdataapplicationscannotberealized.Traditionaldataacquisitioninvolvescreatingtables,filteringdata,collectingdata,andimportingdataintothedatabase.Itlackson-demanddataprocessing.Traditionaldataacquisitionmethodsincludedrivetests,hardware-basedsignalingdatacollection,andsoftware-basedsignalingdatacollection.Thecollecteddatacanbeclassifiedintoreal-timeandnon-real-timedata.Tomakedataacquisition,datatransmission,anddatainteractionmoreefficient,thedataacquisitionfunctionalmodulecanfurthercustomizetimeandspacedataacquisitionbasedondifferentintelligentoptimizationapplicationscenariosandnetworkdeploymentinmobilecommunications.Thecollecteddatacanbestoredinbasicdatawarehouses.

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Thedatacollectionmodulecollectsvarioustypesofdataonpilotnetworksthroughhardware-andsoftware-baseddatacollectionaswellasdrivetests.Thedataincludeswirelessairinterfacedata(ontheUEsideandbasestationside),corenetworkdata,andnetworkmanagementdata.

Wirelessairinterfacedata(ontheUEsideandbasestationside)includesdataatthephysicallayer(L1),datalinklayer(L2),andnetworklayer(L3).Thephysicallayerincludesthedataofdownlinkanduplinksharedchannels,downlinkanduplinkcontrolchannels,randomaccesschannels,andsoundingreferencechannels.Thedatalinklayerincludesthedataofdownlinkscheduling,uplinkscheduling,downlinkcontrol,andchannelstateinformation.Thenetworklayerincludesthedataofbroadcast,paging,andaccessdata.Byextractingtheaforementioneddata,thebasestationsidesetsupcommunicationinterfaces,andsendsreal-timedatatoexternalplatformstoimplementdataacquisitiononthebasestationside.

Corenetworkdataincludescontrol-planeanduser-planedata.Control-planedatamainlyreferstothedatathatcontrolssignalingprotocoltypesinordertocontrolthesetup,maintenance,andreleaseofserviceprocesses.User-planedatareferstoservicedata,suchasvoice,packetservice,instantmessaging,email,andvideodata.

Networkmanagementdataincludesperformance,alarm,andconfigurationdata.Specifically,performancedatareferstotheperformanceinformationcollectedfromdifferentnetworkelements(NEs).AlarmdatareferstovarioustypesofalarmreportsgeneratedbyallNEsfordevicefaults,networkincidentsaswellasforfaultsrelatedtonetworksandservices.ConfigurationdatareferstothebasicinformationaboutNEs.Itismappedtoentitiesandmainlyusedfornetworktopologyinformationmanagement.

Datapreprocessinginvolvesperformingoperations,suchasdirtydataprocessing,time-basedpartitioning,structuredprocessingonunstructureddata,anddataimporttothedatabaseviaETL,onthecollectedrawmobilenetworkdata.

ETLisatooltoextractdatasuchasrelationshipdataandplanedatafilesfromdistributed,heterogeneousdatasourcestothetemporarymiddlelayerforcleaning,conversion,andintegration.Iteventuallyloadsthedatatodatawarehousesordatamartsforonlineanalyticalprocessinganddatamining.

2.3KnowledgeGraph-basedRepresentationandAnalysis

Knowledgegraph,anewwaytorepresentandmanageknowledge,hasemergedinmoreandmoreverticalapplicationfieldsandisplayinganincreasinglyvitalrole[7–11].Networkdataknowledgegraphsconstructedbasedonthemobiledataserveasindustryknowledgegraphsinverticalfieldsofmobilecommunications.Theconstructionofknowledgegraphshashighrequirementsondepth,breadth,andaccuracyofknowledgeregardingmobilecommunications.

Theknowledgegraph-basedcorrelationanalysismoduledrawsonnetworkdataknowledgegraphsforknowledgerepresentation,correlationanalysis,andin-depthmining,providingeffectiveknowledgerulesandknowledgecomputingsupp