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RankSQL: Query Algebra and Optimization for Relational Top-k Queries
Summary: RankSQL extends relational algebra with a rank-relational model and new operators to treat top-k as a first-class data property, akin to membership. It enables a pipelined, incremental ranking engine and uses dimensional enumeration with sampling-based cardinality estimates to optimize rank-aware plans, validated experimentally.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h14ecbf6ec72396d1
Venue
SIGMOD
Year
2005
Pagerank
0.00012938573
Overall Rank
941 | 93.70%
DOI
10.1145/1066157.1066173
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{li_sigmod05,
title = {{RankSQL: Query Algebra and Optimization for Relational Top-k Queries}},
author = {Li, Chengkai and Chang, Kevin Chen-Chuan and Ilyas, Ihab F. and Song, Sumin},
series = {{SIGMOD} '05},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1066157.1066173},
url = {https://dl.acm.org/doi/10.1145/1066157.1066173},
year = {2005}
}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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