GADT: A probability space ADT for representing and querying the physical world

被引：25

作者：

Faradjian, A ^{[1
]}

Gehrke, J ^{[1
]}

Bonnet, P ^{[1
]}

机构：

[1] Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA

来源：

18TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING, PROCEEDINGS | 2002年

关键词：

D O I：

10.1109/ICDE.2002.994710

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Large sensor networks are being widely deployed for measurement, detection, and monitoring applications. Many of these applications involve database systems to store and process data front the physical world. This data has inherent measurement uncertainties that are properly represented by continuous probability distribution functions (pdf's). We introduce a new object-relational data type, the Gaussian ADT GADT, that models physical data as gaussian pdf's, and we show that existing index structures can be used as fast access methods for GADT data. We also present a measure-theoretic model of probabilistic data and evaluate GADT in its light.

引用

页码：201 / 211

页数：11

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