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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)

Paper ID
83
Venue
CIDR
Year
2007
Pagerank
4.1945683e-05
Overall Rank
12,426 | 13.56%
DOI
-

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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
101 ULDBs: Databases with Uncertainty and Lineage 2006 VLDB 0.0004955674
427 Automated Ranking of Database Query Results 2003 CIDR 0.0002352637
760 Creating Probabilistic Databases from Information Extraction Models 2006 VLDB 0.00017053935
1,258 Ordering the Attributes of Query Results 2006 SIGMOD 0.00013013676
1,992 Probabilistic Ranking of Database Query Results 2004 VLDB 9.8462684e-05
2,560 Foundations of Probabilistic Answers to Queries 2005 SIGMOD 8.5402003e-05
5,549 Query Processing over Incomplete Autonomous Databases 2007 VLDB 5.4428494e-05
5,599 Answering Imprecise Queries over Web Databases 2005 VLDB 5.4160529e-05
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