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A Probabilistic Optimization Framework for the Empty-Answer Problem

Summary: Introduces a probabilistic optimization framework for the empty-answer problem to drive interactive query relaxation. It optimizes objectives to suggest relaxations with fewer predicates, offering optimal and approximate solvers and empirical gains over baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10663
Venue
VLDB
Year
2013
Pagerank
7.3955829e-05
Overall Rank
3,197 | 77.77%
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
427 Automated Ranking of Database Query Results 2003 CIDR 0.0002352637
487 Why Not? 2009 SIGMOD 0.00022050218
1,125 How to ConQueR Why-Not Questions 2010 SIGMOD 0.00013845652
1,830 Relaxing Join and Selection Queries 2006 VLDB 0.000103862
1,992 Probabilistic Ranking of Database Query Results 2004 VLDB 9.8462684e-05
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