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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)

Paper ID
560
Venue
CIDR
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,635 | 27.04%
DOI
-

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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}
}

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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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