CLARO: modeling and processing uncertain data streams

CLARO: modeling and processing uncertain data streams Uncertain data streams, where data are incomplete and imprecise, have been observed in many environments. Feeding such data streams to existing stream systems produces results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the claro system that supports stream processing for uncertain data naturally captured using continuous random variables. claro employs a unique data model that is flexible and allows efficient computation. Built on this model, we develop evaluation techniques for relational operators by exploring statistical theory and approximation. We also consider query planning for complex queries given an accuracy requirement. Evaluation results show that our techniques can achieve high performance while satisfying accuracy requirements and outperform state-of-the-art sampling methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

CLARO: modeling and processing uncertain data streams

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Publisher
Springer-Verlag
Copyright
Copyright © 2012 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-011-0261-7
Publisher site
See Article on Publisher Site

Abstract

Uncertain data streams, where data are incomplete and imprecise, have been observed in many environments. Feeding such data streams to existing stream systems produces results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the claro system that supports stream processing for uncertain data naturally captured using continuous random variables. claro employs a unique data model that is flexible and allows efficient computation. Built on this model, we develop evaluation techniques for relational operators by exploring statistical theory and approximation. We also consider query planning for complex queries given an accuracy requirement. Evaluation results show that our techniques can achieve high performance while satisfying accuracy requirements and outperform state-of-the-art sampling methods.

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2012

References

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