II-NEW: Research Infrastructure for Big Spatial and Temporal Data

该基础设施项目响应了管理和分析大空间和时空数据的迫切需求。智能手机、太空望远镜、医疗设备等各种设备不断产生这样的数据。该项目超越了使用通用大数据引擎支持空间和时空数据的想法，通过开发专门的空间和时空大数据系统和算法。本项目获得的硬件基础设施，包括图形处理单元（GPU）和固态硬盘（SSD）等新架构，用于研究和开发各种软硬件平台的空间和时空大数据系统和技术。研究的软件平台范围从传统的并行数据库系统到最先进的分布式计算平台。硬件平台包括商用机器、gpu和ssd的分布式计算节点的大型集群。研究的空间和时空大数据技术包括索引、查询和地图绘制。该项目将其开发的技术封装在公开可用的开源自由软件中。除了在明尼苏达大学内部使用外，所获得的硬件基础设施还可以通过广泛的空间和时空大数据公开web服务向全世界的开发人员、从业者和研究人员开放。

This infrastructure project responds to the urgent need of managing and analyzing big spatial and spatio-temporal data. Such data is continuously produced from various devices including smart phones, space telescopes, and medical devices. The project goes beyond the idea of supporting spatial and spatio-temporal data using general purpose big data engines, by developing specialized spatial and spatio-temporal big data systems and algorithms. The hardware infrastructure acquired in this project, including new architectures such as Graphical Processing Units (GPU) and Solid State Drives (SSD), is used to study and develop spatial and spatio-temporal big data systems and techniques for various software and hardware platforms.The software platforms studied range from traditional parallel database systems to state-of-the-art distributed computing platforms. Hardware platforms include large clusters of distributed computed nodes of commodity machines, GPUs, and SSDs. Spatial and spatio-temporal big data techniques investigated include indexing, querying, and map rendering. The project encapsulates its developed techniques in publicly available open-source free software. In addition to use within the University of Minnesota, the acquired hardware infrastructure is made accessible to developers, practitioners, and researchers worldwide through a wide set of publicly available web services for spatial and spatio-temporal big data.