URank: Formulation and Efficient Evaluation of Top-k Queries in Uncertain Databases
Summary: URank formulates top-k queries over uncertain databases using possible-worlds semantics. It fuses score-based and probability-based ranking via a new processing framework atop existing query engines, enabling efficient search for meaningful top-k results. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,882 | Threshold Query Optimization for Uncertain Data | 2010 | SIGMOD | 4.4289641e-05 |
| 9,165 | Computing All Skyline Probabilities for Uncertain Data | 2009 | PODS | 4.3849295e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 101 | ULDBs: Databases with Uncertainty and Lineage | 2006 | VLDB | 0.0004955674 |
| 1,262 | RankSQL: Query Algebra and Optimization for Relational Top-k Queries | 2005 | SIGMOD | 0.00012986539 |
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