Using Probabilistic Models for Data Management in Acquisitional Environments
Summary: Embed probabilistic models in DBs to exploit correlations for imputing missing values, detecting outliers, and producing low-cost approximate answers over noisy acquisitional data. Propose a unified architecture to integrate these models into sensor/stream acquisition systems and enable correlation-driven queries. (summarized by gpt-5-mini on Feb 09 2026)
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