On Pruning for Top-K Ranking in Uncertain Databases
Summary: Pruning for top-k ranking in uncertain databases under possible-world semantics. Mathematical manipulation of possible worlds exposes prunable work and yields systematic pruning rules; applies to many PRF-based rankings, with thorough experimental evaluation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Chonghai Wang
- 2. Li Yan Yuan
- 3. Jia-Huai You
- 4. Osmar R. Zaiane
- 5. Jian Pei
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 678 | ConQuer: Efficient Management of Inconsistent Databases | 2005 | SIGMOD | 0.00018253213 |
| 1,179 | Probabilistic Skylines on Uncertain Data | 2007 | VLDB | 0.00013457451 |
| 1,609 | A Unified Approach to Ranking in Probabilistic Databases | 2009 | VLDB | 0.00011150935 |
| 1,697 | A Probabilistic Framework for Vague Queries and Imprecise Information in Databases | 1990 | VLDB | 0.00010873473 |
| 1,707 | Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach | 2008 | SIGMOD | 0.00010816111 |
| 3,505 | Consensus Answers for Queries over Probabilistic Databases | 2009 | PODS | 7.0337815e-05 |
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