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dc.contributor.authorBelhajjame, Khalid*
dc.contributor.authorPaton, Norman W.*
dc.contributor.authorHedeler, Cornelia*
dc.contributor.authorFernandes, Alvaro A. A.*
dc.date.accessioned2014-10-30T14:04:40Z
dc.date.available2014-10-30T14:04:40Z
dc.date.issued2015
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/14109
dc.language.isoenen
dc.subjectUser feedbacken
dc.subjectInformation integrationen
dc.subjectClusteringen
dc.subjectCrowden
dc.subjectDataspacesen
dc.subject.ddc005.7en
dc.titleEnabling community-driven information integration through clusteringen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenIt has become widely recognized that user feedback can play a fundamental role in facilitating information integration tasks, e.g., the construction of integration schema and the specification of schema mappings. While promising, existing proposals make the assumption that the users providing feedback expect the same results from the integration system. In practice, however, different users may anticipate different results, due, e.g., to their preferences or application of interest, in which case the feedback they provide may be conflicting, thereby deteriorating the quality of the services provided by the integration system. In this paper, we present clustering strategies for grouping information integration users into groups of users with similar expectations as to the results delivered by the integration system. As well as grouping information integration users, we show that clustering results can be used as inputs to a wide range of functionalities that are relevant in the context of crowd-driven information integration. Specifically, we show that clustering can be used to identify feedback of relevance to a given user by exploiting the feedback provided by other users in the same cluster. We report on evaluation exercises that assess the effectiveness of the clustering strategies we propose, and showcase the benefits community- and crowd-driven information integration can derive from clustering.en
dc.relation.isversionofjnlnameDistributed and Parallel Databases
dc.relation.isversionofjnlvol33
dc.relation.isversionofjnlissue1
dc.relation.isversionofjnldate2015
dc.relation.isversionofjnlpages33-67
dc.relation.isversionofdoi10.1007/s10619-014-7160-zen
dc.identifier.urlsitehttp://dx.doi.org/10.1007/s10619-014-7160-zen
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelOrganisation des donnéesen
dc.relation.forthcomingnonen
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
hal.person.labIds989*
hal.person.labIds61716*
hal.person.labIds61716*
hal.person.labIds61716*
hal.identifierhal-01497340*


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