ITR: Advanced Algorithms for Spatial-Temporal Interactions in Distributed GIS

ITR：分布式 GIS 中时空交互的高级算法

• 批准号: 0081434
• 负责人: Lin Liu
• 金额: $ 48.96万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0081434&HistoricalAwards=false
• 关键词: ITR; Advanced; Algorithms; Spatial; Temporal

This research focuses on the analysis of spatial interactions in distributed GIS environments. Data related to systems of spatial interactions includes spatial flows and locational and attribute data pertaining to origins and destinations. Given that these datasets are distributed across multiple nodes of a computer network, this research aims to: 1) study mechanisms of data partitioning and develop a metadata structure that describes the partition; 2) develop decomposable algorithms for gravity modeling that minimize communication cost; and 3) develop efficient algorithms for learning and classifying flow patterns using distributed data sources. The approach is to let the databases reside at their native sites. The algorithms dynamically decompose themselves into partial computations that are executed at individual database sites, and local results are composed to obtain the same global results that would have been obtained if the databases had been moved to a common site. The algorithms can find the decompositions to match the distribution of data across the network. This research will have significant impact on many problems that need to process distributed data. For example, it can enable GIS systems to analyze spatial-temporal datasets distributed over the Internet without having to move the databases to a common site.

本研究的重点是在分布式GIS环境中的空间相互作用的分析。与空间相互作用系统有关的数据包括空间流动以及与起点和终点有关的位置和属性数据。考虑到这些数据集分布在计算机网络的多个节点上，本研究的目的是：1）研究数据分区的机制，并开发描述分区的元数据结构; 2）开发用于重力建模的可分解算法，以最小化通信成本; 3）开发使用分布式数据源学习和分类流型的有效算法。方法是让数据库驻留在其本地站点。算法动态地将自身分解成在各个数据库站点执行的部分计算，并且局部结果被组成以获得如果数据库已经被移动到公共站点则将获得的相同的全局结果。这些算法可以找到与网络中数据分布相匹配的分解。这一研究成果对解决分布式数据处理的许多问题具有重要意义。例如，它可以使GIS系统分析分布在互联网上的时空数据集，而不必将数据库移动到一个共同的网站。