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)

Paper ID: 558
Venue: CIDR
Year: 2025
Pagerank: 5.357608e-05
Overall Rank: 8,854 | 39.26%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Johannes Wehrstein (Google)
2. Carsten Binnig (Google)
3. Fatma Özcan (Google)
4. Shobha Vasudevan (Google)
5. Yu Gan (Google)
6. Yawen Wang (Google)

BibTeX Citation

@inproceedings{wehrstein_cidr25,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '25},
        title = {{Towards Foundation Database Models}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Wehrstein, Johannes and Binnig, Carsten and Özcan, Fatma and Vasudevan, Shobha and Gan, Yu and Wang, Yawen},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,841	Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems	2025	VLDB	5.2101877e-05
10,139	Leveraging Query Optimizers to Verify the Soundness of LLM-based Query Rewrites for Real-World Workloads, and More!	2026	CIDR	5.093636e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank
43	The Case for Learned Index Structures	2018	SIGMOD	0.00046060254
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035838391
154	Neo: A Learned Query Optimizer	2019	VLDB	0.00028726181
323	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021264788
378	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019638121
397	TURL: Table Understanding through Representation Learning	2021	VLDB	0.00019278189
420	Can Foundation Models Wrangle Your Data?	2023	VLDB	0.00018789852
465	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.0001803934
873	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00013481915
1,174	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00011817414
2,408	Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet	2024	VLDB	8.6154404e-05
2,499	Learning a Partitioning Advisor for Cloud Databases	2020	SIGMOD	8.4993549e-05
2,844	Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction	2022	VLDB	8.0608767e-05
5,792	Pre-training Summarization Models of Structured Datasets for Cardinality Estimation	2022	VLDB	6.0871213e-05
6,357	A Unified Transferable Model for ML-Enhanced DBMS	2022	CIDR	5.9020843e-05

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#	Overall Rank	Paper	Year	Venue
1	2,313	Active Learning for ML Enhanced Database Systems	2020	SIGMOD
2	323	DeepDB: Learn from Data, not from Queries!	2020	VLDB
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4	9,962	Structure-Aware Machine Learning over Multi-Relational Databases	2021	SIGMOD
5	10,557	Database Views as Explanations for Relational Deep Learning	2026	VLDB
6	420	Can Foundation Models Wrangle Your Data?	2023	VLDB
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8	11,516	Data Management Opportunities for Foundation Models	2022	CIDR
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