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Depth Estimation for Ranking Query Optimization

Summary: DEEP offers a principled framework to estimate input depth for rank-join operators from joint score distributions. Novel optimizer statistics algorithms enable accurate, data-aware depth estimation and better cost modeling for rank-join plans. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9641
Venue
VLDB
Year
2007
Pagerank
4.9090969e-05
Overall Rank
6,826 | 52.56%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
2,965 Evaluating Rank Joins with Optimal Cost 2008 PODS 7.8049517e-05
4,072 Proximity Rank Join 2010 VLDB 6.4726748e-05
5,379 Robust and Efficient Algorithms for Rank Join Evaluation 2009 SIGMOD 5.5375923e-05
5,845 Optimal Join Algorithms Meet Top-k 2020 SIGMOD 5.3057391e-05
7,365 Sharing Work in Keyword Search over Databases 2011 SIGMOD 4.7459715e-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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