Holistic query Approximation via RL Modeling
Summary: Presents Holistic Approximate Query Processing: select a compact approximation set to accelerate both aggregate and non-aggregate queries; problem formalized and shown NP-complete. Proposes HARLM, an RL solver that handles large action spaces and generalizes beyond workloads, yielding ~30% accuracy improvement and 10–35× speedups. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Susan B. Davidson
- 2. Tova Milo
- 3. Kathy Razmadze
- 4. Gal Zeevi
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