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Quantifying integration quality using feedback on mapping results

dc.contributor.authorSerrano, Fernando R. S.
dc.contributor.authorFernandes, Alvaro A. A.
dc.contributor.authorChristodoulou, Klitos
dc.description.abstractTraditional data integration delivers high integration quality but requires significant upfront effort because of the need for expensive experts to be involved. The pay-as-you-go approach to data integration aims to reduce this effort by relying on a bootstrap phase where algorithms replace experts in identifying or validating source-to-target semantic correspondences and executable mappings. Since the results of this phase are expected to be of lower quality, a continuous improvement phase is then launched where user feedback is collected and assimilated in order to improve the integration. It is crucial, therefore, to quantify integration quality. This paper presents a solution to this problem using feedback on mapping results as evidence. We contribute a methodology for quantifying integration quality while taking into account the inherent uncertainty of user feedback. The approach is evaluated in synthetic and real-world integration scenarios and shown to accurately and cost-effectively quantify their quality as a conditional probability.en_UK
dc.relation.ispartofseriesProceedings of the 19th International Conference on Information Integration and Web-based Applications & Services;Salzburg, Austria — December 04 - 06, 2017
dc.rightsACM New York, NY, USA ©2017en_UK
dc.subjectResearch Subject Categories::SOCIAL SCIENCES::Statistics, computer and systems science::Informatics, computer and systems science::Information technologyen_UK
dc.subjectTheory of computationen_UK
dc.subjectDatabase Theoryen_UK
dc.subjectData Integrationen_UK
dc.subjectTheory and algorithms for application domainsen_UK
dc.titleQuantifying integration quality using feedback on mapping resultsen_UK
dc.typeWorking Paperen_UK

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