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Data Generation using Declarative Constraints

Summary: Data generation for synthetic databases via declarative cardinality constraints that specify query-result sizes. Efficient algorithms cover a large, practical constraint class, with empirical results showing scalable performance and outperforming prior techniques. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4423
Venue
SIGMOD
Year
2011
Pagerank
9.0926719e-05
Overall Rank
2,291 | 84.07%
DOI
-

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Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
145 Quickly Generating Billion-Record Synthetic Databases 1994 SIGMOD 0.0004138408
372 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.00025354779
512 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00021380733
888 QAGen: Generating Query-Aware Test Databases 2007 SIGMOD 0.00015578618
934 Flexible Database Generators 2005 VLDB 0.00015227409
1,011 ToXgene: A template-based data generator for XML 2002 SIGMOD 0.00014652718
1,483 Simple and Realistic Data Generation 2006 VLDB 0.00011720317
2,035 Generating Example Data for Dataflow Programs 2009 SIGMOD 9.7149269e-05
4,215 Generating XML Structure Using Examples and Constraints 2008 VLDB 6.3527334e-05
4,517 Generating Databases for Query Workloads 2010 VLDB 6.1178732e-05
5,977 Understanding Cardinality Estimation using Entropy Maximization 2010 PODS 5.2455909e-05
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