Experimental Software Systems: Georgraphic Information Systems on High-Speed Clusters: A Vertically Integrated Approach

[9870724] Chase, Jeffrey S. Agarwal, Pankaj K. Duke University实验软件系统：高速集群上的地理信息系统：垂直整合方法地理信息系统（GIS）是科学和政府重要的管理和分析工具。对地理信息系统的实际兴趣刺激了计算机科学几个领域的基础研究。然而，这些研究主要集中在抽象问题上，并且向GIS从业者的技术转移缓慢。该项目旨在通过计算机算法、软件系统和环境科学研究人员的合作，为处理大量空间数据建立一个改进的基础。该研究涉及各个抽象层次的GIS问题——从应用到算法、系统和工具。算法研究建立在几何算法和I/ o高效算法的最新进展上，以解决GIS中出现的问题。系统研究旨在开发和评估网络集群上I/ o密集型应用程序的系统支持，包括存储系统、内存管理和网络方面的实验研究。所有项目成员的共同焦点是支持GIS算法和应用程序的实际实现的工具包。该研究的预期产出是：(1)处理大量空间数据集的新算法，(2)在高速集群上实现GIS算法的全系统实现，以及(3)将这些计算进步转化为环境科学中的GIS实践。

9870724 Chase, Jeffrey S. Agarwal, Pankaj K. Duke University Experimental Software Systems: Geographic Information Systems on High-Speed Clusters: A Vertically Integrated Approach Geographic Information Systems (GIS) are important management and analysis tools in science, and government. Practical interest in GIS has spurred fundamental research in several areas of computer science. However, this research has focused mainly on abstract problems, and technology transfer to GIS practitioners has been slow. This project seeks to develop an improved foundation for dealing with massive spatial data through a collaboration of researchers in computer algorithms, software systems, and environmental sciences. The research deals with GIS problems at all levels of abstraction-from applications to algorithms, systems, and tools. The algorithmic research builds on recent advances in geometric algorithms and I/O-efficient algorithms for problems that arise in GIS. The systems research seeks to develop and evaluate system support for I/O-intensive applications on networked clusters, incorporating experimental research in storage systems, memory management, and networking. The common focal point for all project members is a toolkit that supports practical implementations of GIS algorithms and applications. The expected outputs of the research are: (1) new algorithms for dealing with massive spatial data sets, (2) whole-system implementations of GIS algorithms on high-speed clusters, and (3) transfer of these computational advances into the practice of GIS in the environmental sciences.