cis6930fa11_PayAsYouGoIntegration

cis6930fa11_PayAsYouGoIntegration - Bootstrapping...

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Unformatted text preview: Bootstrapping Pay-As-You-Go Data Integration Systems Anish Das Sarma, Xin Dong, Alon Halevy Christan Grant cgrant@cise.ufl.edu University of Florida October 10, 2011 @cegme (University of Florida) pay-as-you-go data integration October 10, 2011 1 / 27 very technical, dont hate me @cegme (University of Florida) pay-as-you-go data integration October 10, 2011 2 / 27 Outline 1 Background 2 Goal 3 Motivation 4 Example 5 Overview 6 Definitions 7 Generation 8 Consolidation 9 Experiments 10 Conclusion 11 Thank you @cegme (University of Florida) pay-as-you-go data integration October 10, 2011 3 / 27 Background What is Data Integration Data integration systems offer a single-point interface over data sources and possibly heterogeneous data. I Represented by the triple { G , S , M } F G is the Global schema F S is the set of source schemas F M is the mapping between G and S Two main approaches: 1 Global As View G is a collection of mediated views 2 Local As View All local views S are changed to look like G @cegme (University of Florida) pay-as-you-go data integration October 10, 2011 4 / 27 Global As View Simple interface that must be designed in advance Mapping functions must be created for each source to global Queries are reformulated over query to reach data sources Results are retrieved and combined from data sources Does not scale @cegme (University of Florida) pay-as-you-go data integration October 10, 2011 5 / 27 Goal Give best effort answers over data sources while allowing the administrator improve system in a pay-as-you-go fashion. @cegme (University of Florida) pay-as-you-go data integration October 10, 2011 6 / 27 Motivation Many approaches automatically performed the mappings Several plausible mappings may exist / Idea: Lets make the mappings probabilistic and choose the mapping with maximum entropy 1 1 principle of maximum entropy is a postulate which states that, the probability distribution which best represents the current state of knowledge is the one with largest entropy @cegme (University of Florida) pay-as-you-go data integration October 10, 2011 7 / 27 Example from overview Consider source schema S 1 and S 2 both describing people I S 1( name , hPhone , hAddr , oPhone , oAddr ) I S 2( name , phone , addr ) I Possible mappings: M 1( { name } , { phone , hPhone , oPhone } , { addr , hAddress , oAddress } ) M 2( { name } , { phone , hPhone } , { oPhone } , { addr , oAddress } , { hAddress } ) M 3( { name } , { phone , hPhone } , { oPhone } , { addr , hAddress } , { oA } ) M 4( { name } , { phone , oPhone } , { hPhone } , { addr , oA } , { hAddress } ) M 5( { name } , { phone } , { hPhone } , { oPhone } , { addr } , { hAddress } , { oA } ) @cegme (University of Florida) pay-as-you-go data integration October 10, 2011 8 / 27 Example from overview Consider source schema S 1 and S 2 both describing people I S 1( name , hPhone , hAddr , oPhone , oAddr ) I S 2( name , phone , addr ) I Possible mappings: M 1( { name } , { phone ,...
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This note was uploaded on 11/09/2011 for the course CIS 6930 taught by Professor Staff during the Fall '08 term at University of Florida.

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cis6930fa11_PayAsYouGoIntegration - Bootstrapping...

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