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)
Incoming Non-self Citations Over Time
Authors
- 1. Jingyi Yang
- 2. Peizhi Wu
- 3. Gao Cong
- 4. Tieying Zhang
- 5. Xiao He
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,828 | Modeling Shifting Workloads for Learned Database Systems | 2024 | SIGMOD | 4.6407986e-05 |
| 8,523 | Controllable Tabular Data Synthesis Using Diffusion Models | 2024 | SIGMOD | 4.4937074e-05 |
| 9,467 | Database Gyms | 2023 | CIDR | 4.3346412e-05 |
| 10,067 | Detecting Logic Bugs in DBMSs via Equivalent Data Construction | 2026 | SIGMOD | 4.1945683e-05 |
| 10,212 | SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads | 2026 | SIGMOD | 4.1945683e-05 |
| 10,724 | Privacy-Enhanced Database Synthesis for Benchmark Publishing | 2025 | VLDB | 4.1945683e-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.
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