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。这些数据不断地从各种设备中产生，包括智能手机、太空望远镜和医疗设备。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，开发了专门的空间和时空大数据系统和算法。本项目的硬件基础设施包括图形处理器（GPU）和固态硬盘（SSD）等新架构，用于研究和开发适用于各种软件和硬件平台的空间和时空大数据系统和技术，研究的软件平台包括传统的并行数据库系统和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，查询和地图渲染。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一系列面向空间和时空大数据的公开Web服务供全球开发人员、从业人员和研究人员访问。

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.