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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)

Paper ID
9831
Venue
VLDB
Year
2007
Pagerank
5.6966694e-05
Overall Rank
7,123 | 51.14%
DOI
-

Incoming Non-self Citations Over Time

Authors

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
2,585 Evaluating Rank Joins with Optimal Cost 2008 PODS 8.3774452e-05
5,138 Robust and Efficient Algorithms for Rank Join Evaluation 2009 SIGMOD 6.3495536e-05
5,601 Optimal Join Algorithms Meet Top-k 2020 SIGMOD 6.1540123e-05
5,640 Proximity Rank Join 2010 VLDB 6.1384844e-05
7,670 Sharing Work in Keyword Search over Databases 2011 SIGMOD 5.5709469e-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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