Data Management Opportunities for Foundation Models
Summary: Argues foundation models pivot ML from model-centric engineering to data-centric pipelines where the differentiator is training corpora, making data lifecycle management (collection, integration, curation, monitoring) the core DB problem. Highlights DB research opportunities and production challenges. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Laurel Orr (Stanford University)
- 2. Karan Goel (Stanford University)
- 3. Christopher Ré (Stanford University)
BibTeX Citation
@inproceedings{orr_cidr22,
address = {Amsterdam, Netherlands},
series = {{CIDR} '22},
title = {{Data Management Opportunities for Foundation Models}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Orr, Laurel and Goel, Karan and Ré, Christopher},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,147 | Data Management Challenges in Production Machine Learning | 2017 | SIGMOD | 0.00011974846 |
| 1,350 | Automating Large-Scale Data Quality Verification | 2018 | VLDB | 0.00011065626 |
| 2,273 | SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging | 2021 | SIGMOD | 8.8230899e-05 |
| 9,614 | Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation | 2021 | CIDR | 5.2444447e-05 |
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