DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models
Summary: DBEst revisits AQP with ML regression models and density estimators to deliver fast, accurate analytical approximations. It targets low memory, broad aggregate support, and integration with existing systems; TPC-DS and real data show gains over state-of-the-art AQP engines. (summarized by gpt-5-nano on Feb 09 2026)
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