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：一种垂直集成的方法地理信息系统是科学和政府的重要管理和分析工具。对地理信息系统的实际兴趣刺激了计算机科学的几个领域的基础研究。然而，这项研究主要集中在抽象问题上，向地理信息系统实践者转移技术的速度一直很慢。该项目寻求通过计算机算法、软件系统和环境科学方面的研究人员的合作，为处理海量空间数据建立一个更好的基础。这项研究涉及所有抽象层次的地理信息系统问题--从应用程序到算法、系统和工具。算法研究建立在几何算法和针对地理信息系统中出现的问题的I/O高效算法的最新进展的基础上。系统研究旨在开发和评估对联网群集上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地理信息系统算法和应用程序的实际实现的工具包。这项研究的预期成果是：(1)处理海量空间数据集的新算法；(2)在高速集群上实现地理信息系统算法；(3)将这些计算进展转化为地理信息系统在环境科学中的实践。

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.