QUIC: Handling Query Imprecision & Data Incompleteness in Autonomous Databases
Summary: QUIC: decision-theoretic framework that ranks answers by expected relevance, combining a user relevance function with a density model over missing attribute values to handle imprecise queries and incomplete tuples. Describes automated learning of these functions, efficient retrieval techniques, trust mechanisms and a prototype evaluation. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Subbarao Kambhampati
- 2. Garrett Wolf
- 3. Yi Chen
- 4. Hemal Khatri
- 5. Bhaumik Chokshi
- 6. Jianchun Fan
- 7. Ullas Nambiar
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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 |
|---|---|---|---|---|
| 103 | ULDBs: Databases with Uncertainty and Lineage | 2006 | VLDB | 0.00049520051 |
| 427 | Automated Ranking of Database Query Results | 2003 | CIDR | 0.0002350378 |
| 757 | Creating Probabilistic Databases from Information Extraction Models | 2006 | VLDB | 0.00017062337 |
| 1,258 | Ordering the Attributes of Query Results | 2006 | SIGMOD | 0.00013001902 |
| 1,996 | Probabilistic Ranking of Database Query Results | 2004 | VLDB | 9.8374939e-05 |
| 2,564 | Foundations of Probabilistic Answers to Queries | 2005 | SIGMOD | 8.5325119e-05 |
| 5,557 | Query Processing over Incomplete Autonomous Databases | 2007 | VLDB | 5.437603e-05 |
| 5,609 | Answering Imprecise Queries over Web Databases | 2005 | VLDB | 5.4108528e-05 |
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