Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data
Summary: Efficient under- and over-approximation framework for certain and possible answers to ranking, top-k and—uniquely—windowed aggregation queries over uncertain data. Implements physical operators for uncertain sorting/window in PostgreSQL, integrates with existing uncertain-DB methods, and empirically outperforms prior approaches in speed and accuracy. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Su Feng
- 2. Boris Glavic
- 3. Oliver Kennedy
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,364 | A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores | 2025 | SIGMOD | 4.1945683e-05 |
| 10,377 | FastPDB: Towards Bag-Probabilistic Queries at Interactive Speeds | 2025 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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