Towards Foundation Database Models
Summary: Argues for foundation database models: pre-trained, dataset- and task-agnostic models that enable low-overhead transfer to unseen datasets and replace expensive one-off per-task training. Proposes an architecture, shows a prototype feasibility study, and outlines a research roadmap. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Johannes Wehrstein
- 2. Carsten Binnig
- 3. Fatma Özcan
- 4. Shobha Vasudevan
- 5. Yu Gan
- 6. Yawen Wang
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,729 | Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems | 2025 | VLDB | 4.2942813e-05 |
| 9,993 | Leveraging Query Optimizers to Verify the Soundness of LLM-based Query Rewrites for Real-World Workloads, and More! | 2026 | CIDR | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 3,142 | Active Learning for ML Enhanced Database Systems | 2020 | SIGMOD | 7.4815444e-05 |
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| 9,776 | Structure-Aware Machine Learning over Multi-Relational Databases | 2021 | SIGMOD | 4.2856106e-05 |
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| 517 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00021169035 |
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| 11,317 | Data Management Opportunities for Foundation Models | 2022 | CIDR | 4.1945683e-05 |
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