基于雾计算的印度矿产资源信息基础设施管理SDI框架
Fog Computing based SDI Framework for Mineral Resources Information Infrastructure Management in India
摘要：
空间数据基础设施（SDI）是一个重要的概念，用于在网络上共享空间数据。随着空间云计算和雾计算等技术的积累，空间数据基础设施具有更大的发展潜力，已经成为空间数据处理、分析和传输的工具。雾计算是一种范例，其中雾设备有助于增加吞吐量并减少客户端边缘相对于云计算环境的延迟。本文提出并开发了一个基于雾计算的SDI框架，用于从印度矿产资源管理的空间大数据中进行挖掘分析。我们使用嵌入式微处理器Raspberry Pi构建了一个原型。我们通过对印度矿产资源管理的案例研究进行了初步的分析，包括叠加分析。结果表明，雾计算在空间数据分析中具有广阔的应用前景。我们使用开源GIS，即QGIS和QIS插件，以减少从雾节点到云的传输。
英文摘要：
Spatial Data Infrastructure (SDI) is an important concept for sharing spatial data across the web. With cumulative techniques with spatial cloud computing and fog computing, SDI has the greater potential and has been emerged as a tool for processing, analysis and transmission of spatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client with respect to cloud computing environment. This paper proposed and developed a fog computing based SDI framework for mining analytics from spatial big data for mineral resources management in India. We built a prototype using Raspberry Pi, an embedded microprocessor. We validated by taking suitable case study of mineral resources management in India by doing preliminary analysis including overlay analysis. Results showed that fog computing hold a great promise for analysis of spatial data. We used open source GIS i.e. QGIS and QIS plugin for reducing the transmission to cloud from the fog node.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
