GADT: a probability space ADT for representing and querying the physical world
GADT: a probability space ADT for representing and querying the physical world
复制标题
DOI：
10.1109/icde.2002.994710
复制
发表时间：
2002-02
期刊：
影响因子：
--
通讯作者：
Anton Faradjian;J. Gehrke;Philippe Bonnet
中科院分区：
文献类型：
--
作者：
Anton Faradjian;J. Gehrke;Philippe Bonnet
大型传感器网络正在广泛部署用于测量、检测和监控应用。这些应用程序中的许多都涉及数据库系统来存储和处理来自物理世界的数据。这些数据具有固有的测量不确定性，可以通过连续概率分布函数（PDF）正确表示。我们引入了一个新的对象关系抽象数据类型（ADT）-高斯ADT（GADT）-高斯PDF模型的物理数据，我们表明，现有的索引结构可以用作GADT数据的快速访问方法。我们还提出了一个测量理论模型的概率数据和评估GADT在它的光。
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 from the physical world. This data has inherent measurement uncertainties that are properly represented by continuous probability distribution functions (PDFs). We introduce a new object-relational abstract data type (ADT) - the Gaussian ADT (GADT) - that models physical data as Gaussian PDFs, and we show that existing index structures can be used as fast access methods for GADT data. We also present a measurement-theoretic model of probabilistic data and evaluate GADT in its light.