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b谷歌的计算范式MapReduce，并提供分布式文件系统服务，用于处理地理空间图像和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，可以使用基本的图像处理（地图操作）在每个图像块上计算诸如“佛罗里达州拥有植被的陆地面积百分比”这样的查询，然后在包含佛罗里达州陆地块的航空图像块之间进行简单的求和（减少操作）。因此，使用MapReduce范式和大规模光栅图像数据集可以计算出潜在无限数量的此类语义查询。这项探索性工作为地理空间Web服务和MapReduce平台之间提供了一座桥梁，MapReduce平台已经在其他数据密集型应用中取得了成功。这项工作预计将对地理空间数据管理领域产生重大影响，特别是基于地理空间数据的决策支持，使以前不实际的决策支持查询成为可能。这将为在减轻灾害和环境保护等领域实现关键决策支助应用奠定基础。这项工作还为广泛的研究社区提供了独特的综合地理空间数据集。

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.