Managing and Mining Uncertain Data

467 Pages·2009·5.04 MB·English

by  Charu C. Aggarwal (auth.)

Managing and Mining
Uncertain Data
ADVANCES IN DATABASE SYSTEMS
Volume35
SeriesEditors
AhmedK. Elmagarmid AmitP.Sheth
PurdueUniversity WrightStateUniversity
WestLafayette,IN47907 Dayton,Ohio45435
OtherbooksintheSeries:
PRIVACY-PRESERVINGDATAMINING:ModelsandAlgorithmsbyCharuC.AggarwalandPhilip
S.Yu;ISBN:978-0-387-70991-8
SEQUENCEDATAMININGbyGuozhuDong,JianPei;ISBN:978-0-387-69936-3
DATASTREAMS:ModelsandAlgorithms,editedbyCharuC.Aggarwal;ISBN:978-0-387-28759-1
SIMILARITYSEARCH:TheMetricSpaceApproach,P.Zezula,G.Amato,V.Dohnal,M.Batko;ISBN:
0-387-29146-6
STREAM DATA MANAGEMENT, Nauman Chaudhry, Kevin Shaw, Mahdi Abdelguerfi; ISBN:
0-387-24393-3
FUZZYDATABASEMODELINGWITHXML,ZongminMa;ISBN:0-387-24248-1
MININGSEQUENTIALPATTERNSFROMLARGEDATASETS,WeiWangandJiongYang;ISBN:
0-387-24246-5
ADVANCEDSIGNATUREINDEXINGFORMULTIMEDIAANDWEBAPPLICATIONS,Yannis
Manolopoulos,AlexandrosNanopoulos,EleniTousidou;ISBN:1-4020-7425-5
ADVANCESINDIGITALGOVERNMENT:Technology,HumanFactors,andPolicy,editedbyWilliam
J.McIver,Jr.andAhmedK.Elmagarmid;ISBN:1-4020-7067-5
INFORMATION AND DATABASE QUALITY, Mario Piattini, Coral Calero and Marcela Genero;
ISBN:0-7923-7599-8
DATAQUALITY,RichardY.Wang,MostaphaZiad,YangW.Lee:ISBN:0-7923-7215-8
THE FRACTAL STRUCTURE OF DATA REFERENCE: Applications to the Memory Hierarchy,
BruceMcNutt;ISBN:0-7923-7945-4
SEMANTIC MODELS FORMULTIMEDIA DATABASESEARCHING AND BROWSING, Shu-
ChingChen,R.L.Kashyap,andArifGhafoor;ISBN:0-7923-7888-1
INFORMATIONBROKERINGACROSSHETEROGENEOUSDIGITALDATA:AMetadata-based
Approach,VipulKashyap,AmitSheth;ISBN:0-7923-7883-0
Foracompletelistingofbooksinthisseries,gotohttp://www.springer.com
Managing and Mining
Uncertain Data
Editedby
Charu C. Aggarwal
IBMT.J.WatsonResearchCenter
USA
123
Editor
CharuC.Aggarwal
IBMThomasJ.WatsonResearchCenter
19SkylineDrive
Hawthorne,NY10532
[e-mail]
SeriesEditors
AhmedK.Elmagarmid AmitP.Sheth
PurdueUniversity WrightStateUniversity
WestLafayette,IN47907 Dayton,Ohio45435
ISBN978-0-387-09689-6 e-ISBN978-0-387-09690-2
DOI10.1007/978-0-387-09690-2
LibraryofCongressControlNumber:2008939360
(cid:2)c SpringerScience+BusinessMedia,LLC2009
All rights reserved. This work may not be translated or copied in whole orin part without the written
permissionofthepublisher(SpringerScience+Business Media,LLC,233SpringStreet,NewYork,NY
10013,USA),exceptforbriefexcerptsinconnectionwithreviewsorscholarlyanalysis.Useinconnection
withanyformofinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar
ordissimilarmethodologynowknownorhereafterdevelopedisforbidden.
Theuseinthispublicationoftradenames,trademarks,servicemarksandsimilarterms,eveniftheyare
notidentifiedassuch,isnottobetakenasanexpressionofopinionastowhetherornottheyaresubjectto
proprietaryrights.
Printedonacid-freepaper
springer.com
Preface
Uncertaindatamanagementhasseenarevivalininterestinrecentyearsbe-
causeofanumberofnewfieldswhichutilizethiskindofdata. Forexample,in
fields such as privacy-preserving data mining, additional errors may be added
to data in order to mask the identity of the records. Often the data may be
imputed using statistical methods such as forecasting. In such cases, the data
is uncertain in nature. Such data sets may often be probabilistic in nature. In
othercases,databasesmayshowexistentialuncertaintyinwhichoneormore
records may be present or absent from the data set. Such data sets lead to a
numberofuniquechallengesinprocessingandmanagingtheunderlyingdata.
The field of uncertain data management has been studied in the traditional
database literature, but the field has seen a revival in recent years because of
newwaysofcollectingdata. Thefieldofuncertaindatamanagementpresents
a number of challenges in terms of collecting, modeling, representing, query-
ing, indexing and mining the data. We further note that many of these issues
areinter-relatedandcannoteasilybeaddressedindependently. Whilemanyof
theseissueshavebeenaddressedinrecentresearch,theresearchinthisareais
oftenquitevariedinitsscope. Forexample, eventheunderlyingassumptions
of uncertainty are different across different papers. It is often difficult for re-
searchers and students to find a single place containing a coherent discussion
onthetopic.
Thisbookisdesignedtoprovideacoherenttreatmentofthetopicofuncer-
taindatamanagementbyprovidingsurveysofthekeytopicsinthisfield. The
book is structured as an edited volume containing surveys by prominent re-
searchersinthefield. Thechoiceofchaptersiscarefullydesigned,sothatthe
overall content of the uncertain data management and mining field is covered
reasonablywell. Eachchaptercontainsthekeyresearchcontentonaparticular
topic,alongwithpossibleresearchdirections. Thisincludesabroadoverview
of the topic, the different models and systems for uncertain data, discussions
ondatabaseissuesformanaginguncertaindata,andminingissueswithuncer-
taindata. Twoofthemostprominentsystemsforuncertaindatahavealsobeen
describedinthebookinordertoprovideanideahowrealuncertaindataman-
agement systems might work. The idea is to structurally organize the topic,
vi Preface
and provide insights which are not easily available otherwise. It is hoped that
this structural organization and survey approach will be a great help to stu-
dents,researchers,andpractitionersinthefieldofuncertaindatamanagement
andmining.
Contents
Preface v
ListofFigures xv
ListofTables xxi
1
AnIntroductiontoUncertainDataAlgorithmsandApplications 1
CharuC.Aggarwal
1. Introduction 1
2. AlgorithmsforUncertainData 3
3. Conclusions 6
References 7
2
ModelsforIncompleteandProbabilisticInformation 9
ToddJ.Green
1. Introduction 9
2. IncompleteInformationandRepresentationSystems 13
3. -CompletenessandFiniteCompleteness 14
RA
4. ClosureUnderRelationalOperations 18
5. AlgebraicCompletion 19
6. ProbabilisticDatabasesandRepresentationSystems 21
7. Probabilistic?-TablesandProbabilisticOr-SetTables 22
8. Probabilisticc-tables 24
9. QueriesonAnnotatedRelations 25
10. K-Relations 27
11. PolynomialsforProvenance 30
12. QueryContainment 33
13. RelatedWork 34
14. ConclusionandFurtherWork 34
References 37
15. Appendix 41
3
RelationalModelsandAlgebraforUncertainData 45
SumitSarkarandDebabrataDey
1. Introduction 45
viii Contents
2. DifferentProbabilisticDataModels 48
2.1 Point-ValuedProbabilityMeasuresAssignedtoEachTuple 48
2.2 Point-ValuedProbabilityMeasuresAssignedtoAttributesandAt-
tributeSets 52
2.3 Interval-ValuedProbabilityMeasuresAssignedtoAttribute-Values
54
2.4 Interval-valuedProbabilityMeasuresAssignedtoTuples 56
3. ProbabilisticRelationalAlgebra 56
3.1 BasicDefinitions 56
3.2 PrimaryandForeignKeys 58
3.3 RelationalOperations 59
3.4 RelationalAlgebra 62
3.5 IncompleteDistributionandNullValues 63
4. AlgebraicImplicationsoftheDifferentRepresentationsandAssociated
Assumptions 67
4.1 PointValuedProbabilityMeasuresinTuples 68
4.2 Point-ValuedProbabilityMeasuresinAttributes 69
4.3 Interval-ValuedProbabilityMeasuresinAttributes 70
4.4 Interval-ValuedProbabilityMeasuresinTuples 71
4.5 ObservationsonTupleIndependence 72
5. ConcludingRemarks 72
References 75
4
GraphicalModelsforUncertainData 77
AmolDeshpande,LiseGetoorandPrithvirajSen
1. Introduction 78
2. GraphicalModels: Overview 80
2.1 DirectedGraphicalModels: BayesianNetworks 82
2.2 UndirectedGraphicalModels: MarkovNetworks 84
2.3 InferenceQueries 85
3. RepresentingUncertaintyusingGraphicalModels 87
3.1 PossibleWorldSemantics 88
3.2 SharedFactors 91
3.3 RepresentingProbabilisticRelations 91
4. QueryEvaluationoverUncertainData 92
4.1 Example 94
4.2 GeneratingFactorsduringQueryEvaluation 96
4.3 QueryEvaluationasInference 99
4.4 Optimizations 100
5. RelatedWorkandDiscussion 101
5.1 SafePlans 101
5.2 RepresentingUncertaintyusingLineage 102
5.3 ProbabilisticRelationalModels 102
5.4 LiftedInference 104
5.5 ScalableInferenceusingaRelationalDatabase 104
6. Conclusions 105
References 107
Contents ix
5
Trio: ASystemforData,Uncertainty,andLineage 113
JenniferWidom
1. ULDBs: Uncertainty-LineageDatabases 116
1.1 Alternatives 117
1.2 ‘?’ (Maybe)Annotations 117
1.3 Confidences 118
1.4 Lineage 119
1.5 RelationalQueries 121
2. TriQL:TheTrioQueryLanguage 122
2.1 OperationalSemantics 122
2.2 QueryingConfidences 124
2.3 QueryingLineage 124
2.4 DuplicateElimination 125
2.5 Aggregation 126
2.6 ReorganizingAlternatives 127
2.7 HorizontalSubqueries 128
2.8 Query-DefinedResultConfidences 128
2.9 OtherTriQLQueryConstructs 129
3. DataModificationsinTrio 130
3.1 Inserts 130
3.2 Deletes 131
3.3 Updates 131
3.4 DataModificationsandVersioning 133
4. ConfidenceComputation 133
5. AdditionalTrioFeatures 135
6. TheTrioSystem 136
6.1 EncodingULDBData 138
6.2 BasicQueryTranslationScheme 140
6.3 DuplicateElimination 141
6.4 Aggregation 141
6.5 ReorganizingAlternatives 142
6.6 HorizontalSubqueries 142
6.7 Built-InPredicatesandFunctions 143
6.8 Query-DefinedResultConfidences 144
6.9 RemainingConstructs 144
References 147
6
MayBMS:ASystemforManagingLargeProbabilisticDatabases 149
ChristophKoch
1. Introduction 149
2. ProbabilisticDatabases 151
3. QueryLanguageDesiderata 152
4. TheAlgebra 153
5. RepresentingProbabilisticData 159
6. ConceptualQueryEvaluation 164
7. TheMayBMSQueryandUpdateLanguage 170
8. TheMayBMSSystem 175
9. ConclusionsandOutlook 178