Open AccessProceedings Article
Using Probabilistic Models for Data Management in Acquisitional Environments
Amol Deshpande,Carlos Guestrin,Samuel Madden +2 more
- pp 317-328
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TLDR
A suite of techniques based on probabilistic models that are designed to allow database to tolerate noise and loss are discussed, based on exploiting correlations to predict missing values and identify outliers.Abstract:
Traditional database systems, particularly those focused on capturing and managing data from the real world, are poorly equipped to deal with the noise, loss, and uncertainty in data. We discuss a suite of techniques based on probabilistic models that are designed to allow database to tolerate noise and loss. These techniques are based on exploiting correlations to predict missing values and identify outliers. Interestingly, correlations also provide a way to give approximate answers to users at a significantly lower cost and enable a range of new types of queries over the correlation structure itself. We illustrate a host of applications for our new techniques and queries, ranging from sensor networks to network monitoring to data stream management. We also present a unified architecture for integrating such models into database systems, focusing in particular on acquisitional systems where the cost of capturing data (e.g., from sensors) is itself a significant part of the query processing cost.read more
Citations
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Proceedings ArticleDOI
Management of probabilistic data: foundations and challenges
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TL;DR: The foundations of managing data where the uncertainties are quantified as probabilities are described, and some fundamental theoretical result for query evaluation on probabilistic databases is presented.
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Managing and Mining Sensor Data
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ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks
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A unified approach to ranking in probabilistic databases
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Towards correcting input data errors probabilistically using integrity constraints
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