Efficient Search for the Top-k Probable Nearest Neighbors in Uncertain Databases
Summary: Efficiently finds top-k probable nearest neighbors under a general uncertainty model for data and queries. Introduces new query semantics and fast methods to compute marginal NN probabilities; handles dependencies and threshold queries, with experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. George Beskales (University of Waterloo)
- 2. Mohamed A. Soliman (University of Waterloo)
- 3. Ihab F. Ilyas (University of Waterloo)
BibTeX Citation
@article{beskales_vldb08,
title = {{Efficient Search for the Top-k Probable Nearest Neighbors in Uncertain Databases}},
author = {Beskales, George and Soliman, Mohamed A. and Ilyas, Ihab F.},
journal = {PVLDB},
series = {{VLDB} '08},
volume = {1},
number = {1},
pages = {326--337},
doi = {10.14778/1453856.1453895},
url = {https://doi.org/10.14778/1453856.1453895},
year = {2008}
}
Incoming Citations (Sorted by Pagerank)
Showing 17 of 17 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0010828372 |
| 28 | Nearest Neighbor Queries | 1995 | SIGMOD | 0.00052102434 |
| 46 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00044853085 |
| 648 | Progressive Approximate Aggregate Queries with a Multi-Resolution Tree Structure | 2001 | SIGMOD | 0.00015324657 |
| 827 | Minimal Probing: Supporting Expensive Predicates for Top-k Queries | 2002 | SIGMOD | 0.00013769938 |
| 828 | The Design of an Acquisitional Query Processor For Sensor Networks | 2003 | SIGMOD | 0.00013769869 |
| 1,486 | Indexing Multi-Dimensional Uncertain Data with Arbitrary Probability Density Functions | 2005 | VLDB | 0.00010625076 |
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