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)
Incoming Non-self Citations Over Time
Authors
- 1. Sven Helmer (University of Mannheim)
- 2. Till Westmann (University of Mannheim)
- 3. Guido Moerkotte (University of Mannheim)
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 |
|---|---|---|---|---|
| 1,369 | How Soccer Players Would do Stream Joins | 2011 | SIGMOD | 0.0001099892 |
| 5,443 | Generalized Hash Teams for Join and Group-by | 1999 | VLDB | 6.2177595e-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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