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DataSynth: Generating Synthetic Data using Declarative Constraints

Summary: DataSynth uses a declarative, cardinality-constraint abstraction to specify complex synthetic data characteristics. Efficient generation algorithms enable realistic DB instances for testing, masking, and benchmarking; demo on two real-world scenarios. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10216
Venue
VLDB
Year
2011
Pagerank
4.431665e-05
Overall Rank
8,870 | 38.30%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,836 Projection-Compliant Database Generation 2022 VLDB 4.2747054e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 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
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
2,291 Data Generation using Declarative Constraints 2011 SIGMOD 9.0926719e-05
4,517 Generating Databases for Query Workloads 2010 VLDB 6.1178732e-05
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