Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle
Summary: FlorDB incrementally harvests ML pipeline metadata by treating log statements (including post-hoc 'hindsight logging') as first-class context, enabling off–critical-path 'metadata-later' collection without changing developer workflows. Relational views over incomplete metadata let the system dynamically materialize metadata across workflow versions, unifying ad-hoc annotations with feature-store/model-repo use cases. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Rolando Garcia (University of California Berkeley)
- 2. Pragya Kallanagoudar (University of California Berkeley)
- 3. Chithra Anand (University of California Berkeley)
- 4. Sarah E. Chasins (University of California Berkeley)
- 5. Joseph M. Hellerstein (University of California Berkeley)
- 6. Erin Michelle Turner Kerrison (University of California Berkeley)
- 7. Aditya G. Parameswaran (University of California Berkeley)
BibTeX Citation
@inproceedings{garcia_cidr25,
address = {Amsterdam, Netherlands},
series = {{CIDR} '25},
title = {{Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Garcia, Rolando and Kallanagoudar, Pragya and Anand, Chithra and Chasins, Sarah E. and Hellerstein, Joseph M. and Kerrison, Erin Michelle Turner and Parameswaran, Aditya G.},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11 | Implementing Data Cubes Efficiently | 1996 | SIGMOD | 0.00071822821 |
| 1,569 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.00010335423 |
| 1,670 | MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis | 2018 | SIGMOD | 0.00010045615 |
| 1,902 | Ground: A Data Context Service | 2017 | CIDR | 9.506714e-05 |
| 1,937 | Elastic Machine Learning Algorithms in Amazon SageMaker | 2020 | SIGMOD | 9.4524758e-05 |
| 6,525 | Hindsight Logging for Model Training | 2021 | VLDB | 5.8514923e-05 |
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