Depth Estimation for Ranking Query Optimization
Summary: DEEP estimates rank-join input depth—the key cost driver—by modeling the joint score distribution across base tables. Efficient algorithms integrate this principled, data-aware framework into optimizers, outperforming prior estimation techniques. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Karl Schnaitter (University of California Santa Cruz)
- 2. Joshua Spiegel (BEA Systems)
- 3. Neoklis Polyzotis (University of California Santa Cruz)
BibTeX Citation
@article{schnaitter_vldb07,
title = {{Depth Estimation for Ranking Query Optimization}},
author = {Schnaitter, Karl and Spiegel, Joshua and Polyzotis, Neoklis},
journal = {PVLDB},
series = {{VLDB} '07},
pages = {902--913},
year = {2007}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,614 | Evaluating Rank Joins with Optimal Cost | 2008 | PODS | 8.2255837e-05 |
| 4,752 | Optimal Join Algorithms Meet Top-k | 2020 | SIGMOD | 6.434561e-05 |
| 5,229 | Robust and Efficient Algorithms for Rank Join Evaluation | 2009 | SIGMOD | 6.2196057e-05 |
| 5,769 | Proximity Rank Join | 2010 | VLDB | 6.0007533e-05 |
| 7,824 | Sharing Work in Keyword Search over Databases | 2011 | SIGMOD | 5.445981e-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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| 7 | 7,029 | The Case for Deep Query Optimisation | 2020 | CIDR |
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