Investigation of Geospatial Data Management on MapReduce Platform

佛罗里达国际大学的高性能数据库研究中心正在利用Hadoop框架，该框架实现了Google的计算范式MapReduce，并提供分布式文件系统服务，用于提供地理空间图像，并使用异构谓词执行空间查询。这项工作为高性能地理空间查询奠定了基础。例如，诸如“具有植被的佛罗里达州的陆块的百分比”的查询可以使用在每个图像瓦片处的基本图像处理（地图操作）、随后是跨包括佛罗里达陆块的航空图像的瓦片的简单求和（缩减操作）来计算。因此，使用MapReduce范式和大规模栅格图像数据集可以计算出潜在的无限数量的这种语义查询。这项探索性工作正在地理空间网络服务和MapReduce平台之间架起一座桥梁，MapReduce平台在其他数据密集型应用程序中已取得成功，预计这项工作将对地理空间数据管理领域产生重大影响，特别是通过实现以前不切实际的决策支助查询，对基于地理空间数据的决策支助产生重大影响。 这将为减灾和环境保护等领域的关键决策支持应用奠定基础，并为广大研究界提供独一无二的全面地理空间数据收集。

The High Performance Database Research Center at Florida International University is leveraging the Hadoop framework, which implements Google's computational paradigm MapReduce and provides distributed file system services, for serving geospatial imagery and to execute spatial queries with heterogeneous predicates. This work is laying the foundation for high-performance geospatial querying. For instance, queries such as "the percentage of Florida state's land-mass that has vegetation" can be computed using basic image processing (map operation) at each image tile, followed by a simple summation (reduce operation) across tiles that comprise the aerial imagery of the Florida land-mass. A potentially infinite number of such semantic queries can thus be computed using the MapReduce paradigm and a large-scale raster imagery dataset. This exploratory work is providing a bridge between geospatial Web services and the MapReduce platform which has demonstrated success in other data-intensive applications.This work is expected to produce a major impact on the field of geospatial data management and especially decision support based on geospatial data, by enabling decision support queries which were not previously practical. This will provide a foundation to enable critical decision support applications in fields such as disaster mitigation and environmental protection.This work is also providing a uniquely comprehensive collection of geospatial data to a broad research community.