中文摘要：

R 树索引装载体积的平行上的当前的文学有生产空间索引减少的质量更加作为并行增加的劣势。解决这个问题，用流行 MapReduce 框架的装载体积的空间数据的一个新奇方法被建议。MapReduce 联合 Hilbert 曲线和随机的采样方法到平行分区和种类空间数据，因此，它在每个分区平衡空间数据的数字。然后，自底向上的方法被介绍在每个分区简化并且加速分指数构造。三个区域度量标准被用来在不同分区下面测试产生索引的质量。广泛的实验证明产生 R 树用顺序的装载体积的方法与产生 R 树有类似的质量，当实行时间被利用并行更加减少时。

英文摘要：

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.