数据仓库、数据发掘和联机分析处理.（影印版）

Forewordxix

Prefacexxi

Part1.Foundation

Chapter1.IntroductiontoDataWarehousing

1.1WhyAlltheExcitement?

1.2TheNeedforDataWarehousing

1.3ParadigmShift

1.3.1ComputingParadigm

1.3.2BusinessParadigm

1.4BusinessProblemDefinition

1.5OperationalandInformationalDataStores*

1.6DataWarehouseDefinitionandCharacteristics

1.7DataWarehouseArchitecture

1.8ChapterSummary

Chapter2.Client/ServerComputingModelandDataWarehousing

2.1OverviewofClient/ServerArchitecture

2.1.1Host-BasedProcessing

2.1.2Master-SlaveProcessing

2.1.3First-GenerationClient/ServerProcessing

2.1.4Second-GenerationClient/ServerProcessing

2.2ServerSpecializationinClient/ServerComputingEnvironments

2.3ServerFunctions

2.4ServerHardwareArchitecture

2.5SystemConsiderations

2.6RISCversusClSC

2.7MultiprocessorSystems

2.7.1SMPDesign

2.7.2SMPFeatures

2.7.3SMPOperatingSystems

2.8SMPImplementations

Chapter3.ParallelProcessorsandClusterSystems

3.1Distributed-MemoryArchitecture

3.1.1Shared-NothingArchitectures

3.1.2Shared-DiskSystems

3.2ResearchIssues

3.3ClusterSystems

3.4AdvancesinMultiprocessingArchitectures

3.5OptimalHardwareArchitectureforQueryScalability*

3.5.1UniformityofDataAccessTimes

3.5.2SystemArchitectureTaxonomyandQueryExecution

3.6ServerOperatingSystems

3.6.1OperatingSystemRequirements

3.6.2MicrokernelTechnology

3.7OperatingSystemImplementations

3.7.1UNIX

3.7.2Windows/NT

3.7.3OS/2

3.7.4NetWare

3.7.5OSSummary

Chapter4.DistributedDBMSImplementations

4.1ImplementationTrendsandFeaturesofDistributedClient/ServerDBMS

4.1.1RDBMSArchitectureforScalability

4.1.2RDBMSPerformanceandEfficiencyFeatures

4.1.3TypesofParallelism

4.2DBMSConnectivity

4.3AdvancedRDBMSFeatures

4.4RDBMSReliabilityandAvailability

4.4.1Robustness,TransactionsRecovery,andConsistency

4.4.2FaultTolerance

4.5RDBMSAdministration

Chapter5.Client/ServerRDBMSSolutions

5.1State-of-the-MarketOverview

5.2Oracle

5.2.1SystemManagement

5.2.2OracleUniversalServer

5.2.3OracleConTextOption

5.2.4OracleSpatialDataOption

5.3Informix

5.3.1Features

5.3.2InformixUniversalServer

5.4Sybase

5.4.1SYBASESQLServer

5.4.2PerformanceImprovementsinSYBASESystem11

5.5IBM

5.5.1Background

5.5.2DB2UniversalDatabase

5.6Microsoft

5.6.1Background

5.6.2MSSQLServer

5.6.3DataWarehousingandMarketPositioning

Part2DataWarehousing

Chapter6.DataWarehousingComponents

6.1OverallArchitecture

6.2DataWarehouseDatabase

6.3Sourcing,Acquisition,Cleanup,andTransformationTools

6.4Metadata

6.5AccessTools

6.5.1QueryandReportingTools

6.5.2Applications

6.5.3OLAP

6.5.4DataMining

6.5.5DataVisualization

6.6DataMarts

6.7DataWarehouseAdministrationandManagement

6.8InformationDeliverySystem

Chapter7.BuildingaDatawarehouse

7.1BusinessConsiderations:ReturnonInvestment

7.1.1Approach

7.1.2OrganizationalIssues

7.2DesignConsiderations

7.2.1DataContent

7.2.2Metadata

7.2.3DataDistribution

7.2.4Tools

7.2.5PerformanceConsiderations

7.2.6NineDecisionsintheDesignofaDataWarehouse

7.3TechnicalConsiderations

7.3.1HardwarePlatforms

7.3.2DataWarehouseandDBMSSpecialization

7.3.3CommunicationsInfrastructure

7.4ImplementationConsiderations

7.4.1AccessTools

7.4.2DataExtraction,Cleanup,Transformation,andMigration

7.4.3DataPlacementStrategies

7.4.4Metadata

7.4.5UserSophisticationLevels

7.5IntegratedSolutions

7.6BenefitsofDataWarehousing

7.6.1TangibleBenefits

7.6.2IntangibleBenefits

Chapter8.MappingtheDataWarehousetoaMultiprocessorArchitecture

8.1RelationalDatabaseTechnologyforDataWarehouse

8.1.1TypesofParallelism

8.1.2DataPartitioning

8.2DatabaseArchitecturesforParallelProcessing

8.2.1Shared-MemoryArchitecture

8.2.2Shared-DiskArchitecture

8.2.3Shared-NothingArchitecture

8.2.4CombinedArchitecture

8.3ParallelRDBMSFeatures

8.4AlternativeTechnologies

8.5ParallelDBMSVendors

8.5.1Oracle

8.5.2Informix

8.5.3IBM

8.5.4Sybase

8.5.5Microsoft

8.5.6OtherRDBMSProducts

8.5.7SpecializedDatabaseProducts

Chapter9.DBMSSchemasforDecisionSupport

9.1DataLayoutforBestAccess

9.2MultidimensionalDataModel

9.3StarSchema

9.3.1DBAViewpoint

9.3.2PotentialPerformanceProblemswithStarSchemas

9.3.3SolutionstoPerformanceProblems

9.4STARjoinandSTARindex

9.5BitmappedIndexing

9.5.1SYBASEIQ

9.5.2Conclusion

9.6ColumnLocalStorage

9.7ComplexDataTypes

Chapter10.DataExtraction,Cleanup,andTransformationTools

10.1ToolRequirements

10.2VendorApproaches

10.3AccesstoLegacyData

10.4VendorSolutions

10.4.1PrismSolutions

10.4.2SASInstitute

10.4.3CarletonCorporation'sPassportandMetaCenter

10.4.4ValityCorporation

10.4.5EvolutionaryTechnologies

10.4.6InformationBuilders

10.5TransformationEngines

10.5.1Informatica

10.5.2Constellar

Chapter11.Metadata

11.1MetadataDefined

11.2MetadataInterchangeInitiative

11.3MetadataRepository

11.4MetadataManagement

11.5ImplementationExamples

11.5.1PlatinumRepository

11.5.2R&O:TheROCHADERepository

11.5.3PrismSolutions

11.5.4LogicWorksUniversalDirectory

11.6MetadataTrends

Part3.BusinessAnalysis

Chapter12.ReportingandQueryToolsandApplications

12.1ToolCategories

12.1.1ReportingTools

12.1.2ManagedQueryTools

12.1.3ExecutiveInformationSystemTools

12.1.4OLAPTools

12.1.5DataMiningTools

12.2TheNeedforApplications

12.3CognosImpromptu

12.4Applications

12.4.1PowerBuilder

12.4.2Forte

12.4.3InformationBuilders

Chapter13.On-LineAnalyticalProcessing(OLAP)

13.1NeedforOLAP

13.2MultidimensionalDataModel

13.3OLAPGuidelines

13.4MultidimensionalversusMulfirelationalOLAP

13.5CategorizationofOLAPTools

13.5.1MOLAP

13.5.2ROLAP

13.5.3ManagedQueryEnvironment(MQE)

13.6StateoftheMarket

13.6.1CognosPowerPlay

13.6.2IBIFOCUSFusion

13.6.3PilotSoftware

13.7OLAPToolsandtheInternet

13.8Conclusion

Chapter14.PatternsandModels

14.1Definitions

14.1.1WhatIsaPattern?WhatIsaModel?

14.1.2VisualizingaPattern

14.2ANoteonTerminology

14.3WhereAreModelsUsed?

14.3.1Problem1:Selection

14.3.2Problem2:Acquisition

14.3.3Problem3:Retention

14.3.4Problem4:Extension

14.4WhatIsthe"Right"Model?

14.4.1ThePerfectModel

14.4.2MissingData

14.5Sampling

14.5.1TheNecessityofSampling

14.5.2RandomSampling

14.6ExperimentalDesign

14.6.1AvoidingBias

14.6.2MoreonSampling

14.7Computer-IntensiveStatistics

14.7.1Cross-validation

14.7.2JackknifeandBootstrapResampling

14.8PickingtheBestModel

Chapter15.Statistics

15.1Data,Counting,andProbability

15.1.1Histograms

15.1.2TypesofCategoricalPredictors

15.1.3Probability

15.1.4Bayes'Theorem

15.1.5Independence

15.1.6CausalityandCollinearity

15.1.7SimplifyingthePredictors

15.2HypothesisTesting

15.2.1HypothesisTestingonaReal-WorldProblem

15.2.2HypothesisTesting,PValues,andAlpha

15.2.3MakingMistakesinRejectingtheNullHypothesis

15.2.4DegreesofFreedom

15.3ContingencyTables,theChiSquareTest,andNoncausalRelationships

15.3.1ContingencyTables

15.3.2TheChiSquareTest

15.3.3SometimesStrongRelationshipsAreNotCausal

15.4Prediction

15.4.1LinearRegression

15.4.2OtherFormsofRegression

15.5SomeCurrentOfferingsofStatisticsTools

15.5.1SASInstitute

15.5.2SPSS

15.5.3MathSoft

Chapter16.ArtificialIntelligence

16.1DefiningArtificialIntelligence

16.2ExpertSystems

16.3FuzzyLogic

16.4TheRiseandFallofAl

Part4.DataMining

Chapter17.IntroductiontoDataMining

17.1DataMiningHasComeofAge

17:2TheMotivationforDataMiningIsTremendous

17.3LearningfromYourPastMistakes

17.4DataMining?Don'tNeedIt--I'veGotStatistics

17.5MeasuringDataMiningEffectiveness:Accuracy,Speed,andCost

17.6EmbeddingDataMiningintoYourBusinessProcess

17.7TheMoreThingsChange,theMoreTheyRemaintheSame

17.8DiscoveryversusPrediction

17.8.1GoldinThemTharHills

17.8.2Discovery--FindingSomethingYouWeren'tLookingFor

17.8.3Prediction

17.9Overfitting

17.10StateoftheIndustry

17.10.1TargetedSolutions

17.10.2BusinessTools

17.10.3BusinessAnalystTools

17.10.4ResearchAnalystTools

17.11ComparingtheTechnologies

17.11.1BusinessScoreCard

17.11.2ApplicationsScoreCard

17.11.3AlgorithmicScoreCard

Chapter18.DecisionTrees

18.1WhatIsaDecisionTree?

18.2BusinessscoreCard

18.3WheretoUseDecisionTrees

18.3.1Exploration

18.3.2DataPreprocessing

18.3.3Prediction

18.3.4ApplicationsScoreCard

18.4TheGeneralIdea

18.4.1GrowingtheTree

18.4.2WhenDoestheTreeStopGrowing?

18.4.3WhyWouldaDecisionTreeAlgorithmPreventtheTreeFromGrowingIfThereWeren'tEnoughData?

18.4.4DecisionTreesAren'tNecessarilyFinishedafterTheyAreFullyGrown

18.4.5AretheSplitsatEachLeveloftheTreeAlwaysBinaryYes/NoSplits?

18.4.6PickingtheBestPredictors

18.4.7PickingtheRightPredictorValuefortheSplit

18.5HowtheDecisionTreeWorks

18.5.1HandlingHigh-CardinalityPredictorsinID3

18.5.2C4.5EnhancesID3

18.5.3CARTDefinition

18.5.4PredictorsArePickedasTheyDecreasetheDisorderoftheData

18.5.5CARTSplitsUnorderedPredictorsbyImposingOrderonThem

18.5.6CARTAutomaticallyValidatestheTree

18.5.7CARTSurrogatesHandleMissingData

18.5.8CHAID

18.6CaseStudy:PredictingWirelessTelecommunicationsChumwithCART

18.7StrengthsandWeaknesses

18.7.1AlgorithmScoreCard

18.7.2StateoftheIndustry

Chapter19.NeuralNetworks

19.1WhatIsaNeural'Network?

19.1.1Don'tNeuralNetworksLearntoMakeBetterPredictions?

19.1.2AreNeuralNetworksEasytoUse?

19.1.3BusinessscoreCard

19.2WheretoUseNeuralNetworks

19.2.1NeuralNetworksforClustering

19.2.2NeuralNetworksforFeatureExtraction

19.2.3ApplicationsScoreCard

19.3TheGeneralIdea

19.3.1WhatDoesaNeuralNetworkLookLike?

19.3.2HowDoesaNeuralNetworkMakeaPrediction?

19.3.3HowIstheNeuralNetworkModelCreated?

19.3.4HowComplexCantheNeuralNetworkModelBecome?

19.3.5HiddenNodesAreLikeTrustedAdvisorstotheOutputNodes

19.3.6DesignDecisionsinArchitectingaNeuralNetwork

19.3.7DifferentTypesofNeuralNetworks

19.3.8KohonenFeatureMaps

19.3.9HowDoestheNeuralNetworkResembletheHumanBrain?

19.3.10ANeuralNetworkLearnstoSpeak

19.3.11ANeuralNetworkLearnstoDrive

19.3.12TheHumanBrainIsStillMuchMorePowerful

19.4HowtheNeuralNetworkWorks

19.4.1HowPredictionsAreMade

19.4.2HowBackpropagafionLearningWorks

19.4.3DataPreparation

19.4.4CombattingOverfitting

19.4.5ApplyingandTrainingtheNeuralNetwork

19.4.6ExplainingtheNetwork

19.5CaseStudy:PredictingCurrencyExchangeRates

19.5.1TheProblem

19.5.2Implementation

19.5.3Theresults

19.6StrengthsandWeaknessess

19.6.1AlgorithmScoreCard

19.6.2SomeCurrentMarketOfferings

19.6.3Radial-Basis-FunctionNetworks

19.6.4GeneticAlgorithmsandNeuralNetworks

19.6.5SimulatedAnnealingandNeuralNetworks

Chapter20.NearestNeighborandClustering

20.1BusinessScoreCard

20.2WheretoUseClusteringandNearest-NeighborPrediction

20.2.1ClusteringforClarity

20.2.2ClusteringforOutlierAnalysis

20.2.3NearestNeighborforPrediction

20.2.4ApplicationsScoreCard

20.3TheGeneralIdea

20.3.1ThereIsNoBestWaytoCluster

20.3.2HowAreTradeoffsMadeWhenDeterminingWhichRecordsFallintoWhichCluster

20.3.3ClusteringIstheHappyMediumbetweenHomogeneousClustersandtheLowestNumberofClusters

20.3.4WhatIstheDifferencebetweenClusteringandNearest-NeighborPrediction?

20.3.5WhatIsann-DimensionalSpace?

20.3.6HowIstheSpaceforClusteringandNearestNeighborDefined?

20.4HowClusteringandNearest-NeighborPredictionWork

20.4.1Lookingatann-DimensionalSpace

20.4.2HowIs"Nearness'Defined?

20.4.3WeightingtheDimensions:DistancewithaPurpose

20.4.4CalculatingDimensionWeights

20.4.5HierarchicalandNonhierarchicalClustering

20.4.6Nearest-NeighborPrediction

20.4.7KNearestNeighbors--VotingIsBetter

20.4.8GeneralizingtheSolution:PrototypesandSentries

20.5CaseStudy:ImageRecognitionforHumanHandwriting

20.5.1TheProblem

20.5.2SolutionUsingNearest-NeighborTechniques

20.6StrengthsandWeaknessess

20.6.1AlgorithmScoreCard

20.6.2PredictingFutureTrends

Chapter21.GeneticAlgorithms

21.1WhatAreGeneticAlgorithms?

21.1.1HowDoTheyRelatetoEvolution?

21.1.2GeneticAlgorithms,ArtificialLife,andSimulatedEvolution

21.1.3HowCanTheyBeUsedinBusiness?

21.1.4BusinessScoreCard

21.2WheretoUseGeneticAlgorithms

21.2.1GeneticAlgorithmsforOptimization

21.2.2GeneticAlgorithmsforDataMining

21.2.3ApplicationsScoreCard

21.3TheGeneralIdea

21.3.1DoGeneticAlgorithmsGuesstheRightAnswer?

21.3.2AreGeneticAlgorithmsFullyAutomated?

21.3.3CostMinimization:TravelingSalesperson

21.3.4CooperationStrategies:Prisoner'sDilemma

21.4HowtheGeneticAlgorithmWorks

21.4.1TheOverallProcess

21.4.2SurvivaloftheFittest

21.4.3Mutation

21.4.4SexualReproductionandCrossover

21.4.5ExplorationversusExploitation

21.4.6TheSchemaTheorem

21.4.7Epistasis

21.4.8ClassifierSystems

21.4.9RemainingChallenges

21.4.10Sharing:ASolutiontoPrematureConvergence

21.4.11MetalevelEvolution:TheAutomationofParameterChoice

21.4.12ParallelImplementation

21.5CaseStudy:OptimizingPredictiveCustomerSegments

21.6StrengthsandWeaknessess

21.6.1AlgorithmScoreCard

21.6.2StateoftheMarketplace

21.6.3PredictingFutureTrends

Chapter22.RuleInduction

22.1BusinessScoreCard

22.2WheretoUseRuleInduction

22.2.1WhatIsaRule?

22.2.2WhattoDowithaRule

22.2.3Caveat:RulesDoNotImplyCausality

22.2.4TypesofDatabasesUsedforRuleInduction

22.2.5Discovery

22.2.6Prediction

22.2.7ApplicationsScoreCard

22.3TheGeneralIdea

22.3.1HowtoEvaluatetheRule

22.3.2ConjunctionsandDisjunctions

22.3.3Defining"Interestingness"

22.3.4OtherMeasuresofUsefulness

22.3.5RulesversusDecisionTrees

22.4HowRuleInductionWorks

22.4.1ConstructingRules

22.4.2ABrute-ForceAlgorithmforGeneratingRules

22.4.3CombiningEvidence

22.5CaseStudy:ClassifyingU.S.CensusReturns

22.6StrengthsandWeaknesses

22.7CurrentOfferingsandFutureImprovements

Chapter23.SelectingandUsingtheRightTechnique

23.1UsingtheRightTechnique

23.1.1TheDataMiningProcess

23.1.2WhatAlltheDataMiningTechniquesHaveinCommon

23.1.3CasesinWhichDecisionTreesAreLikeNearestNeighbors

23.1.4RuleInductionIsLikeDecisionTrees

23.1.5CouldYouDoLinkAnalysiswithaNeuralNetwork?

23.2DataMiningintheBusinessProcess

23.2.1AvoidingSomeBigMistakesinDataMining

23.2.2UnderstandingtheData

23.3TheCaseforEmbeddedDataMining

23.3.1TheCostofaDistributedBusinessProcess

23.3.2TheBestWaytoMeasureaDataMiningTool

23.3.3TheCaseforEmbeddedDataMining

23.4HowtoMeasureAccuracy,Explanation,andIntegration

23.4.1MeasuringAccuracy

23.4.2MeasuringExplanation

23.4.3MeasuringIntegration

23.5WhattheFutureHoldsforEmbeddedDataMining

Part5.DataVisualizationandOverallPerspective

Chapter24.DataVisualization

24.1DataVisualizationPrinciples

24.2ParallelCoordinates

24.3VisualizingNeuralNetworks

24.4VisualizationofTrees

24.5StateoftheIndustry

24.5.1AdvancedVisualSystems

24.5.2AltaAnalytics

24.5.3BusinessObjects

24.5.4IBM

24.5.5PilotSoftware

24.5.6SiliconGraphics

Chapter25.PuttingItAllTogether

25.1DesignforScalability

25.2DataQuality

25.3ImplementationNotes

25.3.1OperationalDataStores

25.3.2DataMarts

25.3.3StarSchema

25.4MakingtheMostofYourWarehouse

25.5TheDataWarehousingMarket

25.6CostsandBenefits

25.6.1BigData--BiggerReturns

25.6.2LawofDiminishingReturns

25.7AUnifyingViewofBusinessInformation

25.8What'sNext

25.8.1DistributedWarehouseEnvironments

25.8.2UsingtheInternetorIntranetforInformationDelivery

25.8.3Object-RelationalDatabases

25.8.4VeryLargeDatabases(VLDBs)

25.9Conclusion

AppendixA.Glossary

AppendixB.BigData--BetterReturns:LeveragingYourHiddenDataAssetstoImproveROI

AppendixC.Dr.E.F.Codd's12GuidelinesforOLAP

AppendixD.10MistakesforDataWarehousingManagerstoAvoidBibliography605

Index609