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)
Incoming Non-self Citations Over Time
Authors
- 1. Amol Deshpande (University of Maryland)
- 2. Carlos Guestrin (Carnegie Mellon University)
- 3. Samuel R. Madden (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{deshpande_cidr05,
address = {Amsterdam, Netherlands},
series = {{CIDR} '05},
title = {{Using Probabilistic Models for Data Management in Acquisitional Environments}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Deshpande, Amol and Guestrin, Carlos and Madden, Samuel R.},
year = {2005}
}
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