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Diag-Join: An Opportunistic Join Algorithm for 1:N Relationships

Summary: Diag-Join exploits time-of-creation clustering to enable an opportunistic join for 1:N relations. It outperforms block-wise nested-loop join, GRACE hash join, and index nested-loop join on clustered data and provides an analytical cost model. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h71727dfb2b7303d9
Venue
VLDB
Year
1998
Pagerank
5.7073875e-05
Overall Rank
6,690 | 55.03%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{helmer_vldb98,
        title = {{Diag-Join: An Opportunistic Join Algorithm for 1:N Relationships}},
        author = {Helmer, Sven and Westmann, Till and Moerkotte, Guido},
        journal = {PVLDB},
        series = {{VLDB} '98},
        year = {1998}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,387 How Soccer Players Would do Stream Joins 2011 SIGMOD 0.00010825457
5,530 Generalized Hash Teams for Join and Group-by 1999 VLDB 6.0935245e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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