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dc.contributor.authorGiacometti, Arnaud
HAL ID: 3762
dc.contributor.authorMarcel, Patrick
HAL ID: 4561
ORCID: 0000-0003-3171-1174
dc.contributor.authorNegre, Elsa
dc.contributor.authorSoulet, Arnaud
HAL ID: 740484
ORCID: 0000-0001-8597-3909
dc.date.accessioned2011-10-11T10:34:46Z
dc.date.available2011-10-11T10:34:46Z
dc.date.issued2011
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/7158
dc.language.isoenen
dc.subjectMDX queriesen
dc.subjectcollaborative filteringen
dc.subjectquery recommendationen
dc.subjectdiscovery-driven analysisen
dc.subjectOLAPen
dc.subject.ddc005en
dc.titleQuery Recommendations for OLAP Discovery-Driven Analysisen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenRecommending database queries is an emerging and promising field of research and is of particular interest in the domain of OLAP systems, where the user is left with the tedious process of navigating large datacubes. In this paper, the authors present a framework for a recommender system for OLAP users that leverages former users’ investigations to enhance discovery-driven analysis. This framework recommends the discoveries detected in former sessions that investigated the same unexpected data as the current session. This task is accomplished by (1) analysing the query log to discover pairs of cells at various levels of detail for which the measure values differ significantly, and (2) analysing a current query to detect if a particular pair of cells for which the measure values differ significantly can be related to what is discovered in the log. This framework is implemented in a system that uses the open source Mondrian server and recommends MDX queries. Preliminary experiments were conducted to assess the quality of the recommendations in terms of precision and recall, as well as the efficiency of their on-line computation.en
dc.relation.isversionofjnlnameInternational Journal of Data Warehousing and Mining
dc.relation.isversionofjnlvol7en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2011
dc.relation.isversionofjnlpages1-25en
dc.relation.isversionofdoihttp://dx.doi.org/10.4018/jdwm.2011040101en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherIGI Globalen
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen


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