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Diag-Join: An Opportunistic Join Algorithm for 1:N Relationships | Full Paper (PDF)
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Time 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.
If we are able to take advantage of time- of-creation clustering, then theperformance evaluation reveals the superiority of Diag-Join over standard join algorithms like block-wise nested-loop join, GRACE hash join, and index nested-loop join.
We also present an analytical cost model for Diag-Join.
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References, where available, link to the DBLP on the World Wide Web.
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@inproceedings{DBLP:conf/vldb/HelmerWM98, author = {Sven Helmer and Till Westmann and Guido Moerkotte}, editor = {Ashish Gupta and Oded Shmueli and Jennifer Widom}, title = {Diag-Join: An Opportunistic Join Algorithm for 1:N Relationships}, booktitle = {VLDB'98, Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA}, publisher = {Morgan Kaufmann}, year = {1998}, isbn = {1-55860-566-5}, pages = {98-109}, crossref = {DBLP:conf/vldb/98}, bibsource = {DBLP, http://dblp.uni-trier.de} }
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