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Approximate aggregation techniques for sensor databases (2004)
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| Venue: | In ICDE |
| Citations: | 301 - 6 self |
BibTeX
@INPROCEEDINGS{Considine04approximateaggregation,
author = {Jeffrey Considine and Feifei Li and George Kollios and John Byers},
title = {Approximate aggregation techniques for sensor databases},
booktitle = {In ICDE},
year = {2004},
pages = {449--460}
}
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Abstract
In the emerging area of sensor-based systems, a significant challenge is to develop scalable, fault-tolerant methods to extract useful information from the data the sensors collect. An approach to this data management problem is the use of sensor database systems, exemplified by TinyDB and Cougar, which allow users to perform aggregation queries such as MIN, COUNT and AVG on a sensor network. Due to power and range constraints, centralized approaches are generally impractical, so most systems use in-network aggregation to reduce network traffic. Also, aggregation strategies must provide fault-tolerance to address the issues of packet loss and node failures inherent in such a system. An unfortunate consequence of standard methods is that they typically introduce duplicate values, which must be accounted for to compute aggregates correctly. Another consequence of loss in the network is that exact aggregation is not possible in general. With this in mind, we investigate the use of approximate in-network aggregation using small sketches. Our contributions are as follows: 1) we generalize well known duplicateinsensitive sketches for approximating COUNT to handle SUM (and by extension, AVG and other aggregates), 2) we present and analyze methods for using sketches to produce accurate results with low communication and computation overhead (even on low-powered CPUs with little storage and no floating point operations), and 3) we present an extensive experimental validation of our methods. 1
Keyphrases
approximate aggregation technique sensor database sensor-based system useful information little storage sensor network exact aggregation network traffic significant challenge extensive experimental validation computation overhead unfortunate consequence in-network aggregation duplicate value duplicateinsensitive sketch standard method accurate result data management problem aggregation strategy sensor database system node failure range constraint approximate in-network aggregation small sketch low communication low-powered cpu packet loss analyze method aggregation query fault-tolerant method floating point operation
