Optimal Top-k Generation of Attribute Combinations based on Ranked Lists
Summary: Top-k,m queries over grouped ranked attribute lists: pick one attribute per group via matching IDs in top-m tuples. Introduces the first provably instance-optimal algorithm with optimizations to cut accesses and memory, validated on real applications. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jiaheng Lu
- 2. Pierre Senellart
- 3. Chunbin Lin
- 4. Xiaoyong Du
- 5. Shan Wang
- 6. Xinxing Chen
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0015496097 |
| 17 | Optimizing Multi-Feature Queries for Image Databases | 2000 | VLDB | 0.00096067547 |
| 1,096 | Minimal Probing: Supporting Expensive Predicates for Top-k Queries | 2002 | SIGMOD | 0.00014120512 |
| 2,883 | Joining Ranked Inputs in Practice | 2002 | VLDB | 7.9656673e-05 |
| 3,807 | Supporting Ad-hoc Ranking Aggregates | 2006 | SIGMOD | 6.747576e-05 |
| 3,908 | Progressive and Selective Merge: Computing Top-K with Ad-hoc Ranking Functions | 2007 | SIGMOD | 6.6392878e-05 |
| 5,575 | Structure and Content Scoring for XML | 2005 | VLDB | 5.4264592e-05 |
| 7,276 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD | 4.7798595e-05 |
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