摘要
近年来,空间在线分析处理(spatial on-line analytical processing,SOLAP)技术已逐渐应用于遥感多维分析研究领域,但其计算性能仍面临大规模数据的挑战。本文借鉴数据密集型计算模式MapReduce在OLAP领域的相关应用研究,提出一种基于该模式扩展的遥感数据SOLAP立方体模型。在数据分级分块基础上,对现有模型在分布式环境下进行了适应性改进,并在Map-Reduce支持下,通过引入多维地图代数将该模型中的SOLAP计算转化为基于栅格块粒度的并行地图代数操作。以遥感旱情应用为例阐述了模型的构建与应用过程,并实现了原型。试验结果证明了该模型在大规模数据处理情况下具有较好加速性能与可扩展性。
SOLAP has been applied to multi-dimensional analysis of remote sensing data recently.However,its computation performance faces a considerable challenge from the large-scale dataset.A geo-raster cube model extended by Map-Reduce is proposed,which refers to the application of Map-Reduce(a data-intensive computing paradigm)in the OLAP field.In this model,the existing methods are modified to adapt to distributed environment based on the multi-level raster tiles.Then the multi-dimensional map algebra is introduced to decompose the SOLAP computation into multiple distributed parallel map algebra functions on tiles under the support of Map-Reduce.The drought monitoring by remote sensing data is employed as a case study to illustrate the model construction and application.The prototype is also implemented,and the performance testing shows the efficiency and scalability of this model.
出处
《测绘学报》
EI
CSCD
北大核心
2014年第6期627-636,共10页
Acta Geodaetica et Cartographica Sinica
基金
水利部公益性行业科研专项(201001046)
