Current literature on parallel bulk-loading of R-tree index has the disadvantage that the quality of produced spatial index decrease considerably as the parallelism increases. To solve this problem, a novel method of bulk-loading spatial data using the popular MapReduce framework is proposed. MapReduce combines Hilbert curve and random sampling method to parallel partition and sort spatial data, thus it balances the number of spatial data in each partition. Then the bottom-up method is introduced to simplify and accelerate the sub-index construction in each parti- tion. Three area metrics are used to test the quality of generated index under different partitions. The extensive experiments show that the generated R-trees have the similar quality with the gener- ated R-tree using sequential bulk-loading method, while the execution time is reduced considerably by exploiting parallelism.
针对传统的空间数据处理软件的缺点,采用空间数据抽象库(Geospatial Data Abstraction Library,GDAL)实现不同来源的月球空间数据共享。根据月球空间数据的特点,采用空间数据交换模式设计和实现了包括桌面、网络和网络处理服务(WPS)3种形式的月球空间数据转换服务,有效解决了行星数据系统等月球空间数据的共享问题。该方案在实际应用中取得了良好的效果,下一步,将研究采用并行技术,以提高数据的转换效率。
