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RALF: Accuracy-Aware Scheduling for Feature Store Maintenance

Summary: Addresses costly, frequently stale feature/embedding updates in feature stores by introducing accuracy-aware scheduling that leverages downstream error feedback instead of one-size-fits-all policies. RALF formalizes feature-store regret and schedules updates to minimize downstream prediction error, achieving up to 32.7% error reduction or 1.6× compute savings on large-scale workloads. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13735
Venue
VLDB
Year
2024
Pagerank
4.2827012e-05
Overall Rank
9,786 | 31.93%
DOI
10.14778/3632093.3632116

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Rank Citing Paper Year Venue Pagerank
8,080 Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines 2024 VLDB 4.5911668e-05
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