不連續(xù)及不穩(wěn)定數(shù)據(jù)管理英文

1、Efficient Management of Inconsistent and Uncertain DataRene J. MillerUniversity of TorontoContributorsAriel Fuxman, PhD ThesisMicrosoft Search LabsJim Gray SIGMOD 2008 Dissertation AwardPeriklis Andritsos, PhDJiang Du, MSElham Fazli, MSDiego Fuxman, UndergradDirty DatabasesThe presence of dirty data

2、 is a major problem in enterprisesTraditional solution: data cleaning3No. I dont see Any problem with the dataLimitations of Data CleaningSemi-automatic processRequires highly-qualified domain experts Time consumingMay not be possible to wait until the database is cleanOperational systems answer que

3、ries assuming clean dataOur WorkIdentify classes of queries for which we can obtain meaningful answers from potentially dirty databasesShow how to do it efficiently and reusing existing database technology5Why is this Business Intelligence?Business intelligence (BI) refers to technologies, applicati

4、ons and practices for the collection, integration, analysis, and presentation of information.The goal of BI is to support better decision making, based on information.DBMS should provide meaningful query answers even over data that is dirtyOutline Introduction Semantics for dirty databases Contribut

5、ions Conclusions7Outline Introduction Semantics for dirty databases Contributions Conclusions8A Data Integration ExampleIntegrating customer data9SalesShippingCustomer SupportWeb FormsDemographic DataIntegratedCustomerDatabaseMatching and Merging10CustidPeterNameAddressIncomePeter Yarrow276 College

6、Street40KPaul Stookey100 Bloor Street400KMary Travers20 Union Street110KNameAddressIncomePeter Yarrow276 College Street40KPaul Stookey100 Bloor Street400KMary Travers20 Union Street110KCustidPeterNameAddressIncomePeter Yarrow276 College St.200KPaul Stookey100 Bloor St.400KMary Travers20 Union St.130

7、KNameAddressIncomePeter Yarrow276 College St.200KPaul Stookey100 Bloor St.400KMary Travers20 Union St.130KWebSalesMatching and merging are two fundamental tasks in data integration NameAddressIncomePeter Yarrow276 College Street200KPaul Stookey100 Bloor St.400KMary Travers20 Union St.130KNameAddress

8、IncomePeter Yarrow276 College Street40KPaul Stookey100 Bloor Street400KMary Travers20 Union Street110KTrue Disagreement Between Sources11CustidPeterWebSalesWhats Peters salary?CustidPeterInconsistent Integrated DatabasesIn the absence of complete resolution rules12custidincomePeter40KPaul 400KMary11

9、0KcustidincomePeter 200KPaul400KMary130KcustidincomePeter40KPeter200KPaul400KMary110KMary130KSATISFY custid KEYVIOLATES custid KEYWebSalesInconsistent Integrated DatabaseCustidincomePeter40KPeter200KPaul400KMary110KMary130KCustidincomePeter40KPeter200KPaul400KMary110KMary130KCustidincomePeter40KPete

10、r200KPaul400KMary110KMary130KQuery: “Get customers who make more than 100K”13saleswebsales/websaleswebPeter,Paul,MaryAre we sure that we want to offer a card to Peter?Example: Offering a Platinum credit cardQuerying Inconsistent DatabasesAggressive: Get customers who possibly make more than 100KPete

11、r, Paul, Mary Conservative: Get customers who certainly make more than 100KPaul, Mary14Querying Inconsistent DatabasesFormal SemanticsRelated to semantics for querying incomplete data Imielinski Lipski 84, Abiteboul Duschka 98Possible world: “complete” databasesConsistent answersProposed by Arenas,

12、Bertossi, and Chomicki in 1999Corresponds to conservative semanticsPossible world: “consistent” databases15custidincomePeter40KPeter200KPaul400KMary110KMary130KcustidincomePeter40KPeter200KPaul400KMary110KMary130KcustidincomePeter40KPeter200KPaul400KMary110KMary130KcustidincomePeter40KPeter200KPaul4

13、00KMary110KMary130KcustidincomePeter40KPeter200KPaul400KMary110KMary130K16Peter40KPaul400KMary110KPeter40KPaul400KMary130KPeter200KPaul400KMary110KPeter200KPaul400KMary130Ksaleswebsales/websaleswebInconsistent databaseRepairsKey: custidConsistent Answers17CONSISTENT ANSWERSAnswers obtainedno matter

14、which repair we choosePeter40KPaul400KMary110KPeter40KPaul400KMary130KPeter200KPaul400KMary110KPeter200KPaul400KMary130KQuery=“Get customers who make more than 100K”qqqqCONSISTENT ANSWER=Paul,MaryRepairsConsistent AnswersPaulMaryPaulMaryPeterPaulMaryPeterPaulMaryPaulMaryPaulMaryPeterPaulMaryPeterPau

15、lMaryOutline Introduction Semantics for dirty databases Contributions Conclusions18When We StartedSemantics well understoodProblemPotentially HUGE number of repairs!Negative results Chomicki et al 02, Arenas et al. 01, Cali et al 04 Few tractability results Arenas et al. 99, Arenas et al. 01Logic pr

16、ogramming approaches Bravo and Bertossi 03, Eiter et al. 03Expressive queries and constraintsComputationally expensiveApplicable only to small databases with small number of inconsistencies19Our Proposal: ConQuer20Commercial databaseengineSQL query q KeysRewrittenSQL query Q*ConQuersRewriting Algori

17、thmInconsistentdatabaseConsistent answer to qClass of Rewritable QueriesConQuer handles a broad class of SPJ queries withSet semanticsBag semantics, grouping, and aggregationNo restrictions onNumber of relationsNumber of joinsConditions or built-in predicatesKey-to-key joinsThe class is “maximal”21W

18、hy not all SPJ queries?Some SPJ queries cannot be rewritten into SQLConsistent query answering is coNP-complete even for some SPJ queries and key constraintsMaximality of ConQuers classMinimal relaxations lead to intractabilityRestrictions only onNonkey-to-nonkey joinsSelf joinsNonkey-to-key joins t

19、hat form a cycle22Example: A Rewritable QuerySELECT c_custkey, c_name, sum(l_extendedprice * (1 - l_discount) as revenue, c_acctbal, n_name, c_address, c_phone, c_commentFROM customer, orders, lineitem, nationWHERE c_custkey = o_custkey and l_orderkey = o_orderkey and o_orderdate = 1993-10-01 and o_

20、orderdate date(1993-10-01) + 3 MONTHS and l_returnflag = R and c_nationkey = n_nationkeyGROUP BY c_custkey, c_name, c_acctbal, c_phone, n_name, c_address, c_commentORDER BY revenue desc23TPC-H Query 10Rewritings Can Get Quite ComplexRewriting of TPC-H Query 10Can this rewriting be executed efficient

21、ly?1.7 overhead20 GB database, 5% inconsistency Experimental EvaluationGoalsQuantify the overhead of the rewritingsAssess the scalability of the approach Determine sensitivity of the rewritten queries to level of inconsistency of the instanceQueries and databasesRepresentative decision support queri

22、es (TPC-H benchmark)TPC-H databases, altered to introduce inconsistenciesDatabase parametersdatabase sizepercentage of the database that is inconsistentconflicts per key value (in inconsistent portion)2526Worst Case5.8 overheadSelectivity 98.56 %Size (GB)5 % inconsistent tuples2 conflicts per incons

23、istent key valueScalabilityBest Case1.2 overheadSelectivity 0.001 %Contributions TheoryFormal characterization of a broad class of queries For which computing consistent answers is tractable under key constraintsThat can be rewritten into first-order/SQLQuery rewriting algorithms for a class of Sele

24、ct-Project-Join queries With set semanticsWith bag semantics, grouping, and aggregationMaximality of the class of queries27Contributions PracticeImplementation of ConQuer Designed to compute consistent answers efficientlyMultiple rewriting strategiesExperimental validation of efficiency and scalability Representative queries from TPC-HLarge databases28Uncertain DatacustidincomePeter40KPaul 400KMary110KcustidincomePeter 200KPaul400KMary130KcustidincomePeter40KPeter200KPaul400KMary110KMary130KWebSalesIntegrated Database0.30.7PROVENANCE INFORMATION(e.g., source re