Fields as a generic data type for big spatial data

被引：13

作者：

Camara, Gilberto ^{[1
,2
]}

Egenhofer, Max J. ^{[3
]}

Ferreira, Karine ^{[1
]}

Andrade, Pedro ^{[1
]}

Queiroz, Gilberto ^{[1
]}

Sanchez, Alber ^{[2
]}

Jones, Jim ^{[2
]}

Vinhas, Lubia ^{[1
]}

机构：

[1] Image Processing Division, National Institute for Space Research (INPE), São José dos Campos

[2] Institute for Geoinformatics (ifgi), University of Münster

[3] National Center for Geographic Information and Analysis and School of Computing and Information Science, University of Maine, Orono, ME

来源：

| 1600年 / Springer Verlag卷 / 8728期

基金：

美国国家科学基金会; 巴西圣保罗研究基金会;

关键词：

Remote sensing;

D O I：

10.1007/978-3-319-11593-1_11

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent different types of spatiotemporal data, such as trajectories, time series, remote sensing and, climate data. To assess its power of generality, we show how to represent existing algebras for spatial data with the Fields data type. The paper also argues that array databases are the best support for processing big spatial data and shows how to use the Fields data type with array databases. © Springer International Publishing Switzerland 2014.

引用

页码：159 / 172

页数：13

共 31 条

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