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

该基础设施项目响应了对海量空间和时空数据进行管理和分析的迫切需求。这些数据是从包括智能手机、太空望远镜和医疗设备在内的各种设备上连续产生的。该项目通过开发专门的空间和时空大数据系统和算法，超越了使用通用大数据引擎支持空间和时空数据的想法。该项目获得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硬件平台的空间和时空大数据系统和技术，所研究的软件平台从传统的并行数据库系统到最先进的分布式计算平台。硬件平台包括商用计算机、GPU和固态硬盘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，世界各地的开发人员、从业者和研究人员还可以通过一系列公开可用的空间和时空大数据Web服务来访问所获得的硬件基础设施。

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.