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Design metrics for data warehouse evolution

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dc.contributor.author Papastefanatos, G en
dc.contributor.author Vassiliadis, P en
dc.contributor.author Simitsis, A en
dc.contributor.author Vassiliou, Y en
dc.date.accessioned 2014-03-01T02:45:13Z
dc.date.available 2014-03-01T02:45:13Z
dc.date.issued 2008 en
dc.identifier.issn 03029743 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/32215
dc.subject Data Warehouse en
dc.subject Data Warehouse Design en
dc.subject.other Alternative configurations en
dc.subject.other Like queries en
dc.subject.other Software modules en
dc.subject.other Warehouse designs en
dc.subject.other Administrative data processing en
dc.subject.other Data warehouses en
dc.subject.other Database systems en
dc.subject.other Design en
dc.subject.other Electric resistance en
dc.subject.other Multi agent systems en
dc.subject.other Warehouses en
dc.title Design metrics for data warehouse evolution en
heal.type conferenceItem en
heal.identifier.primary 10.1007/978-3-540-87877-3-32 en
heal.identifier.secondary http://dx.doi.org/10.1007/978-3-540-87877-3-32 en
heal.publicationDate 2008 en
heal.abstract During data warehouse design, the designer frequently encounters the problem of choosing among different alternatives for the same design construct. The behavior of the chosen design in the presence of evolution events is an important parameter for this choice. This paper proposes metrics to assess the quality of the warehouse design from the viewpoint of evolution. We employ a graph-based model to uniformly abstract relations and software modules, like queries, views, reports, and ETL activities. We annotate the warehouse graph with policies for the management of evolution events. The proposed metrics are based on graph-theoretic properties of the warehouse graph to assess the sensi tivity of the graph to a set of possible events. We evaluate our metrics with experiments over alternative configurations of the same warehouse schema. © 2008 Springer Berlin Heidelberg. en
heal.journalName Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.identifier.doi 10.1007/978-3-540-87877-3-32 en
dc.identifier.volume 5231 LNCS en
dc.identifier.spage 440 en
dc.identifier.epage 454 en


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