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RALF: Accuracy-Aware Scheduling for Feature Store Maintenance

Summary: RALF schedules costly feature/embedding recomputation using downstream prediction-error feedback, query access patterns, and a regret objective rather than fixed refresh policies. Experiments show up to 32.7% lower error or 1.6× lower compute cost. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13923
Venue
VLDB
Year
2024
Pagerank
5.1924403e-05
Overall Rank
9,940 | 31.81%
DOI
10.14778/3632093.3632116

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wooders_vldb24,
        title = {{RALF: Accuracy-Aware Scheduling for Feature Store Maintenance}},
        author = {Wooders, Sarah and Mo, Xiangxi and Narang, Amit and Lin, Kevin and Stoica, Ion and Hellerstein, Joseph M. and Crooks, Natacha and Gonzalez, Joseph E.},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {3},
        pages = {563--576},
        doi = {10.14778/3632093.3632116},
        url = {https://doi.org/10.14778/3632093.3632116},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
7,847 Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines 2024 VLDB 5.5330423e-05
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