Query-Guided Resolution in Uncertain Databases
Summary: Query-guided resolution for uncertain databases using an oracle to certify tuple correctness. Provenance-based tracking and active-learning selectivity minimize oracle calls while preserving correct outputs for mission-critical queries. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Osnat Drien (Bar-Ilan University)
- 2. Matanya Freiman (Bar-Ilan University)
- 3. Antoine Amarilli (Télécom Paris)
- 4. Yael Amsterdamer (Bar-Ilan University)
BibTeX Citation
@inproceedings{drien_sigmod23,
title = {{Query-Guided Resolution in Uncertain Databases}},
author = {Drien, Osnat and Freiman, Matanya and Amarilli, Antoine and Amsterdamer, Yael},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589325},
url = {https://dl.acm.org/doi/10.1145/3589325},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,170 | A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores | 2025 | SIGMOD | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 29 of 29 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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