Conceptual Modeling of Data with Provenance by David

224 Pages·2011·9.48 MB·English

ConceptualModelingofDatawithProvenance
by
DavidWilliamArcher
Adissertationsubmittedinpartialfulfillmentofthe
requirementsforthedegreeof
DoctorofPhilosophy
in
ComputerScience
DissertationCommittee:
LoisM.L.Delcambre,Chair
DavidMaier
LeonardShapiro
MarkJones
CharlesWeber
PortlandStateUniversity
(cid:13)c 2011
i
ABSTRACT
Traditional database systems manage data, but often do not address its prove-
nance. In the past, users were often implicitly familiar with data they used, how it
wascreated(andhencehowitmightbeappropriatelyused),andfromwhichsources
it came. Today, users may be physically and organizationally remote from the data
they use, so this information may not be easily accessible to them. In recent years,
several models have been proposed for recording provenance of data. Our work is
motivatedbyopportunitiestomakeprovenanceeasytomanageandquery. Forexam-
ple, current approaches model provenance as expressions that may be easily stored
alongside data, but are difficult to parse and reconstruct for querying, and are diffi-
cult to query with available languages. We contribute a conceptual model for data
andprovenance,andevaluatehowwellitaddressestheseopportunities. Wecompare
theexpressivepowerofourmodel’slanguagetothatofothermodels. Wealsodefine
a benchmark suite with which to study performance of our model, and use this suite
to study key model aspects implemented on existing software platforms. We dis-
cover some salient performance bottlenecks in these implementations, and suggest
future work to explore improvements. Finally, we show that our implementations
cancomprisealogicalmodelthatfaithfullysupportsourconceptualmodel.
ii
DEDICATION
To Cynthia
iii
Acknowledgements
This research was a team effort, and was successful because of the contributions of
everyoneinvolved. Firstandforemostamongcontributorswasmyadvisor,Professor
LoisM.L.Delcambre,whoconsistentlypushedmetothinkaboutthebigpicture,al-
waysbroughttothetablenewideasforustoconsider,andwasapatientandthorough
reviewer of this work. The members of my thesis committee also contributed new
perspectives, ideas that I had missed, and excellent constructive critique. For these
I thank Professor David Maier, Professor Leonard Shapiro, Professor Mark Jones,
and Professor Charles Weber. I also appreciate the ideas, critiques, and inputs of
other faculty and students in the PSU DataLab research group: Kristin Tufte, Rafael
J.Fernandez-Moctezuma,JeremySteinhauer,NickRayner,ScottBrittell,andJames
Terwilliger.
This work was supported in part by the National Science Foundation, grant IIS-
0534762, and by DARPA. Support for this work came from a number of others,
including the PSU Computer Science Department staff: Beth Holmes, Kathi Lee,
andReneRemillard.
Irecognizeanddeeplyappreciatethecontributions,patience,andencouragement
ofmywife,CynthiaL.Archer,PhD.Withouther,thisachievementwouldhavebeen
impossible.
iv
Contents
Abstract i
Dedication ii
Acknowledgements iii
ListofTables x
ListofFigures xi
1 Introduction 1
1.1 ExampleSettingsforProvenance . . . . . . . . . . . . . . . . . . . 3
1.1.1 DevelopmentofTargetedCancerTherapies . . . . . . . . . 3
1.1.2 CorporateBudgetPlanning . . . . . . . . . . . . . . . . . . 5
1.1.3 BattlefieldInformationManagement . . . . . . . . . . . . . 7
1.1.4 OpportunitiestoEnhanceProvenanceModels . . . . . . . . 8
1.2 WhereCurrentProvenanceModelsFallShortforOurSettings . . . 11
1.3 ResearchGoals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 ConceptualModelOverview 18
2.1 ModelFundamentals . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 StructureofaRelationalMMPDataFace . . . . . . . . . . . . . . 25
2.2.1 ExternalSourcesofData . . . . . . . . . . . . . . . . . . . 25
v
2.3 StructureofanExampleMMPProvenanceModel . . . . . . . . . . 26
2.3.1 ContinuityofExistingData . . . . . . . . . . . . . . . . . . 30
2.3.2 GranularityandInheritanceofProvenance . . . . . . . . . . 31
2.4 InteractingwithandVisualizingMMP . . . . . . . . . . . . . . . . 33
2.4.1 TheMMPLanguage . . . . . . . . . . . . . . . . . . . . . 33
2.4.2 DataSemanticsoftheMMPLanguage . . . . . . . . . . . . 35
2.4.2.1 Data Definition, Manipulation, and Confidence
Operations . . . . . . . . . . . . . . . . . . . . . 35
2.4.2.2 QueryOperations . . . . . . . . . . . . . . . . . 40
2.4.3 ConfidenceLanguage . . . . . . . . . . . . . . . . . . . . . 40
2.4.4 PredicateLanguageforSelectionandProjectionOperators . 41
2.5 ProvenanceCreationSemanticsoftheMMPLanguage . . . . . . . 45
2.6 ProvenanceGraphsasVisualizationTools . . . . . . . . . . . . . . 46
2.7 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3 FormalizingtheConceptualModel 50
3.1 ModelingEvolvingData: Faces . . . . . . . . . . . . . . . . . . . . 51
3.2 ModelingTheOutsideWorld: ExternalSourceReferents . . . . . . 52
3.3 ModelingDataDerivation: ProvenanceLinks . . . . . . . . . . . . 52
3.3.1 Operation-inducedProvenanceLinks . . . . . . . . . . . . 53
3.3.2 ContinuityProvenanceLinks . . . . . . . . . . . . . . . . . 55
3.4 ModelingOperationsAppliedtoData: Revisions . . . . . . . . . . 56
3.5 ModelingCreationofExternalSourceReferents . . . . . . . . . . . 58
3.6 Single-revisionandSource-CreationImpactonDataandProvenance 59
3.6.1 DDLRevisionsandSourceCreations . . . . . . . . . . . . 60
3.6.1.1 CreateRelation . . . . . . . . . . . . . . . . . . . 60
3.6.1.2 CreateSource . . . . . . . . . . . . . . . . . . . 61
3.6.1.3 CreateAttribute . . . . . . . . . . . . . . . . . . 61
vi
3.6.1.4 DropRelation . . . . . . . . . . . . . . . . . . . 61
3.6.1.5 DropAttribute . . . . . . . . . . . . . . . . . . . 62
3.6.2 DMLandDCLRevisions . . . . . . . . . . . . . . . . . . . 62
3.6.2.1 InsertValue . . . . . . . . . . . . . . . . . . . . . 63
3.6.2.2 DropValue . . . . . . . . . . . . . . . . . . . . . 63
3.6.2.3 InsertTuple . . . . . . . . . . . . . . . . . . . . . 64
3.6.2.4 DropTuple . . . . . . . . . . . . . . . . . . . . . 64
3.6.2.5 PasteValue . . . . . . . . . . . . . . . . . . . . . 65
3.6.2.6 PasteTuple . . . . . . . . . . . . . . . . . . . . . 66
3.6.2.7 PasteRelation . . . . . . . . . . . . . . . . . . . 66
3.6.2.8 ConfirmValueandDoubtValue . . . . . . . . . . 67
3.6.3 QueryRevisions . . . . . . . . . . . . . . . . . . . . . . . 68
3.6.3.1 SelectionOperatorProvenance . . . . . . . . . . 69
3.6.3.2 ProjectionOperatorProvenance . . . . . . . . . . 70
3.6.3.3 CartesianProductOperatorProvenance . . . . . . 72
3.6.3.4 UnionOperatorProvenance . . . . . . . . . . . . 72
3.6.4 ProvenanceforResultsofGeneralMMPQueries . . . . . . 73
3.7 AccessingProvenanceInformation . . . . . . . . . . . . . . . . . . 75
3.7.1 ProvenanceGraphs . . . . . . . . . . . . . . . . . . . . . . 77
3.7.1.1 Preliminaries: TracingContinuityandInheritance 78
3.7.1.2 DefiningProvenanceGraphs . . . . . . . . . . . . 81
3.7.2 QueryingProvenance . . . . . . . . . . . . . . . . . . . . . 83
3.7.2.1 ExampleofProvenancePredicateEvaluation . . . 89
3.7.3 ProvenancePolynomials . . . . . . . . . . . . . . . . . . . 90
3.7.3.1 RepresentingOperationsinProvenancePolynomials 96
3.7.3.2 Evaluating Plurality of Support with Provenance
Polynomials . . . . . . . . . . . . . . . . . . . . 97
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3.7.4 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . 98
4 ConceptualModelEvaluation 100
4.1 EvaluatingMMPAgainstGapsintheLiterature . . . . . . . . . . . 101
4.2 EvaluatingMMPAgainstNeedsinTargetSettings . . . . . . . . . . 105
4.3 RelativeExpressivenessofAlgebraicProvenanceRepresentations . 109
4.4 RelativeExpressivenessofProvenance-relatedQueries . . . . . . . 111
4.4.1 ProvenanceSelectionQueries . . . . . . . . . . . . . . . . 112
4.4.2 QuerysetforExpressivenessComparison . . . . . . . . . . 112
4.4.3 ComparisonofExpressiveness . . . . . . . . . . . . . . . . 114
4.4.3.1 Buneman’sWhy-provenancemodel . . . . . . . . 114
4.4.3.2 Trio . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.4.3.3 Green’smodel . . . . . . . . . . . . . . . . . . . 116
4.4.3.4 Examplequery1 . . . . . . . . . . . . . . . . . . 116
4.4.3.5 Examplequery2 . . . . . . . . . . . . . . . . . . 116
4.4.3.6 Examplequery3 . . . . . . . . . . . . . . . . . . 117
4.4.3.7 Examplequery4 . . . . . . . . . . . . . . . . . . 117
4.4.3.8 Examplequery5 . . . . . . . . . . . . . . . . . . 118
4.4.3.9 Examplequery6 . . . . . . . . . . . . . . . . . . 118
4.4.3.10 Examplequery7 . . . . . . . . . . . . . . . . . . 119
4.4.3.11 Examplequery8 . . . . . . . . . . . . . . . . . . 119
4.4.3.12 Examplequery9 . . . . . . . . . . . . . . . . . . 120
4.4.3.13 Conclusions About Expressiveness of Provenance
SelectionQueries . . . . . . . . . . . . . . . . . . 120
4.5 OtherAdvantagesofMMPRelativetoOtherModels . . . . . . . . 121
4.5.1 AccessingAncestorsandOperationalHistoryofData . . . . 121
4.5.2 ComputingForward-LookingProvenance . . . . . . . . . . 122
4.6 RelativeComplexityofProvenance-relatedQueries . . . . . . . . . 124
viii
4.7 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5 CharacterizingPerformanceofImplementationChoicesforMMP 129
5.1 BenchmarksandMetrics . . . . . . . . . . . . . . . . . . . . . . . 129
5.1.1 Dataquerybenchmark . . . . . . . . . . . . . . . . . . . . 131
5.1.1.1 Datastructureforrelationaldatabasetesting . . . 132
5.1.1.2 Datastructureforgraphdatabasetesting . . . . . 132
5.1.1.3 Dataqueryworkload . . . . . . . . . . . . . . . . 133
5.1.2 Provenancequerybenchmark . . . . . . . . . . . . . . . . . 135
5.1.2.1 Provenancestructureforrelationaldatabasetesting 136
5.1.2.2 Provenancestructureforgraphdatabasetesting . . 137
5.1.2.3 Provenancequeryworkload . . . . . . . . . . . . 137
5.1.3 PerformanceComparisonMetrics . . . . . . . . . . . . . . 139
5.2 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . 141
5.3 ExperimentsandResults . . . . . . . . . . . . . . . . . . . . . . . 141
5.3.1 RelationalDataQueryTests . . . . . . . . . . . . . . . . . 142
5.3.1.1 TestforDataQuery1 . . . . . . . . . . . . . . . 143
5.3.1.2 TestforDataQuery2 . . . . . . . . . . . . . . . 145
5.3.1.3 TestforDataQuery3 . . . . . . . . . . . . . . . 147
5.3.1.4 TestforDataQuery4 . . . . . . . . . . . . . . . 148
5.3.1.5 TestResultsUsingWarm-StartCaches . . . . . . 148
5.3.1.6 ConclusionsonDataTests . . . . . . . . . . . . . 149
5.3.2 ProvenancePredicateTests . . . . . . . . . . . . . . . . . . 149
5.3.2.1 ConclusionsforProvenanceTests . . . . . . . . . 152
5.3.3 ImplicationsforMMPImplementations . . . . . . . . . . . 153
5.4 OtherIdeasforAcceleratingMMPImplementations . . . . . . . . . 153
5.5 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
ix
6 ALogicalModeltoSupportMMPImplementation 157
6.1 TransformingConceptualModelsintoLogicalModels . . . . . . . 159
6.1.1 EquivalenceClassesofLanguageOperators . . . . . . . . . 161
6.1.1.1 Class1: DropAttribute . . . . . . . . . . . . . . 162
6.1.1.2 Class2: InsertTuple . . . . . . . . . . . . . . . . 163
6.1.1.3 Class3: PasteTuple . . . . . . . . . . . . . . . . 163
6.1.1.4 Class4: Queries . . . . . . . . . . . . . . . . . . 164
6.2 FaithfulSupportofMMPbyMMPL . . . . . . . . . . . . . . . . . 165
6.2.1 BasisCaseforInduction . . . . . . . . . . . . . . . . . . . 171
6.2.2 InductiveCase . . . . . . . . . . . . . . . . . . . . . . . . 172
6.2.2.1 DataPortionofInductiveCase . . . . . . . . . . . 172
6.2.2.2 ProvenancePortionofInductiveCase . . . . . . . 175
6.3 EfficiencyoftheLogicalModel . . . . . . . . . . . . . . . . . . . 182
6.4 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7 RelatedWork 186
7.1 TheOpenProvenanceModel . . . . . . . . . . . . . . . . . . . . . 188
7.2 ProvenanceModelsintheLiterature . . . . . . . . . . . . . . . . . 190
7.2.1 LineageTracingforGeneralDataWarehouseTransformations 191
7.2.2 AnnotationManagementSystems . . . . . . . . . . . . . . 192
7.2.3 CPDB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
7.2.4 Trio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
7.2.5 Panda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
7.2.6 Orchestra . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
7.3 ComparingExpressivenessofPopularProvenanceModels . . . . . 196
7.4 PerformanceofProvenanceModels . . . . . . . . . . . . . . . . . 197
7.5 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . 197