Presentation | 2007-07-03 The Design of a Probabilistic Inference Sensor Database System Hideyuki KAWASHIMA, Hiroyuki KITAGAWA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Applications of ubiquitous sensor networks are widely spread to natural environment monitoring, civil environment monitoring, or human condition monitoring. A database technology to support them is in network processing techniques. It executes computation of data manipulation operations in a sensor network and improves both the performance and the quality of query result, reducing power consumption. So far, most of in network processing techniques were for aggregations. However, the purpose of building sensor networks is the recognition of the physical world, and the aggregations are not very useful for the recognitions. This paper picks up a probabilistic inference technique Bayesian networks, and proposes a method to apply it for in network processing. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | In Network Processing / Bayesian Networks / Sensor Networks |
Paper # | DE2007-80 |
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Committee | DE |
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Conference Date | 2007/6/25(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Data Engineering (DE) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | The Design of a Probabilistic Inference Sensor Database System |
Sub Title (in English) | |
Keyword(1) | In Network Processing |
Keyword(2) | Bayesian Networks |
Keyword(3) | Sensor Networks |
1st Author's Name | Hideyuki KAWASHIMA |
1st Author's Affiliation | Graduate School of Systems and Information Engineering, Department of Computer Science, University of Tsukuba() |
2nd Author's Name | Hiroyuki KITAGAWA |
2nd Author's Affiliation | Graduate School of Systems and Information Engineering, Department of Computer Science, University of Tsukuba |
Date | 2007-07-03 |
Paper # | DE2007-80 |
Volume (vol) | vol.107 |
Number (no) | 131 |
Page | pp.pp.- |
#Pages | 6 |
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