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)
Incoming Non-self Citations Over Time
Authors
- 1. Chengkai Li
- 2. Kevin Chen-Chuan Chang
- 3. Ihab F. Ilyas
- 4. Sumin Song
Incoming Citations (Sorted by Pagerank)
Showing 34 of 34 citing papers.
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Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,191 | Efficient Rank Join with Aggregation Constraints | 2011 | VLDB | 4.1945683e-05 |
| 7,468 | Boolean + Ranking: Querying a Database by K-Constrained Optimization | 2006 | SIGMOD | 4.7210446e-05 |
| 5,373 | Robust and Efficient Algorithms for Rank Join Evaluation | 2009 | SIGMOD | 5.5425231e-05 |
| 7,276 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD | 4.7798595e-05 |
| 4,711 | Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach | 2006 | VLDB | 5.9790683e-05 |
| 674 | Supporting Top-k Join Queries in Relational Databases | 2003 | VLDB | 0.00018327585 |
| 10,970 | Relational Algorithms for Top-k Query Evaluation | 2024 | SIGMOD | 4.1945683e-05 |
| 805 | Evaluating Top-k Selection Queries | 1999 | VLDB | 0.00016437265 |
| 2,393 | Rank-aware Query Optimization | 2004 | SIGMOD | 8.9016542e-05 |
| 6,882 | RankSQL: Supporting Ranking Queries in Relational Database Management Systems | 2005 | VLDB | 4.8963901e-05 |