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dc.contributor.authorHelmer, S
dc.contributor.authorWestmann, T
dc.contributor.authorMoerkotte, G
dc.contributor.editorGupta, A
dc.date.accessioned2015-07-21T09:20:12Z
dc.date.available2015-07-21T09:20:12Z
dc.date.issued1998
dc.identifier.isbn1-55860-566-5
dc.identifier.urihttp://ub-madoc.bib.uni-mannheim.de/788
dc.identifier.urihttp://hdl.handle.net/10863/1170
dc.description.abstractTime of creation is one of the predominant (often implicit) clustering strategies found not only in Data Warehouse systems: line items are created together with their corresponding order, objects are created together with their subparts and so on. The newly created data is then appended to the existing data. We present a new join algorithm, called Diag-Join, which exploits time-of-creation clustering. The performance evaluation reveals its superiority over standard join algorithms like nested-loop join and GRACE hash join. We also present an analytical cost model for Diag-Join.en_US
dc.publisherMorgan Kaufmann Publishers Inc.en_US
dc.titleDiag-Join: An Opportunistic Join Algorithm for 1:N Relationshipsen_US
dc.typeBook chapteren_US
dc.date.updated2015-07-21T08:32:17Z
dc.publication.titleVLDB '98: Proceedings of the 24rd International Conference on Very Large Data Bases
dc.description.fulltextnoneen_US


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