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The design of an acquisitional query processor for sensor networks

Madden, Samuel, Franklin, Michael J., Hellerstein, Joseph M., Hong, Wei·2003·Proceedings of the 2003 ACM SIGMOD international conference on Management of data

Abstract

We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.

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Metadata

Authors
Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W.
Venue
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Publisher
ACM
Published
2003-06-09
Pages
491-502
ISSN
0730-8078 (ISSN-L)
OpenAlex
W2153259545
Cited by
927
References
0
FWCI
56.33
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: The design of an acquisitional query processor for sensor networks. https://app.cochat.ai/papers/10.1145/872757.872817
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Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2003). The design of an acquisitional query processor for sensor networks. Proceedings of the 2003 ACM SIGMOD international conference on Management of data. https://doi.org/10.1145/872757.872817

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.