Rank-aware Query Optimization
Summary: Rank-aware query optimization framework integrating rank-join operators into relational engines, extending System R's dynamic programming with ranking-driven enumeration and pruning. Proposes a probabilistic model to estimate input cardinality and rank-join cost—the first to optimize input size for rank aggregation—validated on an open-source DBMS. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ihab F. Ilyas
- 2. Rahul Shah
- 3. Walid G. Aref
- 4. Jeffrey Scott Vitter
- 5. Ahmed K. Elmagarmid
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 454 | An Overview of Query Optimization in Relational Systems | 1998 | PODS | 0.00022734812 |
| 674 | Supporting Top-k Join Queries in Relational Databases | 2003 | VLDB | 0.00018327585 |
| 10,880 | RankPQO: Learning-to-Rank for Parametric Query Optimization | 2025 | VLDB | 4.1945683e-05 |
| 7,166 | Ranked Enumeration of Join Queries with Projections | 2022 | VLDB | 4.8124491e-05 |
| 2,961 | Evaluating Rank Joins with Optimal Cost | 2008 | PODS | 7.8110394e-05 |
| 5,373 | Robust and Efficient Algorithms for Rank Join Evaluation | 2009 | SIGMOD | 5.5425231e-05 |
| 6,829 | Depth Estimation for Ranking Query Optimization | 2007 | VLDB | 4.9136064e-05 |
| 12,191 | Efficient Rank Join with Aggregation Constraints | 2011 | VLDB | 4.1945683e-05 |
| 6,882 | RankSQL: Supporting Ranking Queries in Relational Database Management Systems | 2005 | VLDB | 4.8963901e-05 |
| 1,262 | RankSQL: Query Algebra and Optimization for Relational Top-k Queries | 2005 | SIGMOD | 0.00012986539 |