The massive amount of data and large variety of data distributions in the big data era call for access methods that are efficient in both query processing and index management, and over both practical and worst-case workloads. To address this need, we revisit two classic multidimensional access methods—the R-tree and the space-filling curve. We propose a novel R-tree packing strategy based on space-filling curves. This strategy produces R-trees with an asymptotically optimal I/O complexity for window queries in the worst case. Experiments show that our R-trees are highly efficient in querying both real and synthetic data of different distributions. The proposed strategy is also simple to parallelize, since it relies only on sorting. We propose a parallel algorithm for R-tree bulk-loading based on the proposed packing strategy and analyze its performance under the massively parallel communication model. To handle dynamic data updates, we further propose index update algorithms that process data insertions and deletions without compromising the optimal query I/O complexity. Experimental results confirm the effectiveness and efficiency of the proposed R-tree bulk-loading and updating algorithms over large data sets.

中文翻译：

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用空间填充曲线打包 R 树

大数据时代的海量数据和种类繁多的数据分布需要在查询处理和索引管理以及实际工作负载和最坏情况工作负载方面都高效的访问方法。为了满足这一需求，我们重新审视了两种经典的多维访问方法——R-tree 和空间填充曲线。我们提出了一种基于空间填充曲线的新型 R-tree 打包策略。这种策略在最坏的情况下为窗口查询生成具有渐近最优 I/O 复杂度的 R-tree。实验表明，我们的 R-tree 在查询不同分布的真实数据和合成数据方面非常高效。所提出的策略也很容易并行化，因为它只依赖于排序。基于所提出的打包策略，我们提出了一种 R-tree 批量加载的并行算法，并分析了其在大规模并行通信模型下的性能。为了处理动态数据更新，我们进一步提出了处理数据插入和删除而不影响最优查询 I/O 复杂度的索引更新算法。实验结果证实了所提出的 R-tree 批量加载和更新算法在大型数据集上的有效性和效率。