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SAM: Database Generation from Query Workloads with Supervised Autoregressive Models

Summary: SAM: supervised autoregressive model to generate synthetic DBs from query workloads. Linear in workload size, attributes, and domain; unbiased sampling with join-key assignment recovers full outer joins, yielding data matching input cardinalities. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hd4add1322c54b653
Venue
SIGMOD
Year
2022
Pagerank
6.2262056e-05
Overall Rank
5,209 | 64.98%
DOI
10.1145/3514221.3526168

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yang_sigmod22,
        title = {{SAM: Database Generation from Query Workloads with Supervised Autoregressive Models}},
        author = {Yang, Jingyi and Wu, Peizhi and Cong, Gao and Zhang, Tieying and He, Xiao},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3526168},
        url = {https://dl.acm.org/doi/10.1145/3514221.3526168},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank Citing Paper Year Venue Pagerank
5,865 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9659203e-05
6,680 Controllable Tabular Data Synthesis Using Diffusion Models 2024 SIGMOD 5.7104433e-05
9,454 SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads 2026 SIGMOD 5.1736782e-05
9,706 Database Gyms 2023 CIDR 5.1376763e-05
10,412 Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] 2026 SIGMOD 4.9793485e-05
10,415 Automated Discovery of Test Oracles for Database Management Systems Using LLMs 2026 SIGMOD 4.9793485e-05
10,432 DBugScribe: Automatic Database Bug Reproduction from Community Reports 2026 SIGMOD 4.9793485e-05
10,559 Detecting Logic Bugs in DBMSs via Equivalent Data Construction 2026 SIGMOD 4.9793485e-05
11,354 Privacy-Enhanced Database Synthesis for Benchmark Publishing 2025 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

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

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
254 On the Design and Quantification of Privacy Preserving Data Mining Algorithms 2001 PODS 0.00023020039
286 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.0002211981
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
371 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019829769
386 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019444411
406 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019045544
512 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017050173
747 QAGen: Generating Query-Aware Test Databases 2007 SIGMOD 0.00014283935
1,060 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012224575
1,433 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010677711
2,165 Data Synthesis based on Generative Adversarial Networks 2018 VLDB 8.933677e-05
2,413 Data Generation using Declarative Constraints 2011 SIGMOD 8.5094338e-05
3,052 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7052471e-05
4,090 Generating Databases for Query Workloads 2010 VLDB 6.8126942e-05
4,329 Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration 2020 VLDB 6.6597797e-05
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