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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
10217
Venue
VLDB
Year
2011
Pagerank
4.4274172e-05
Overall Rank
8,870 | 38.36%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,835 Projection-Compliant Database Generation 2022 VLDB 4.2706095e-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 0.00041403894
512 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00021385343
882 QAGen: Generating Query-Aware Test Databases 2007 SIGMOD 0.00015634206
933 Flexible Database Generators 2005 VLDB 0.00015220613
2,295 Data Generation using Declarative Constraints 2011 SIGMOD 9.0842571e-05
4,441 Generating Databases for Query Workloads 2010 VLDB 6.1795515e-05
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