Spatial indexes are crucial for the analysis of the increasing amounts of spatial data, for example generated through IoT applications. The plethora of indexes that has been developed in recent decades has primarily been optimised for disk. With increasing amounts of memory even on commodity machines, however, moving them to main memory is an option. Doing so opens up the opportunity to use additional optimizations that are only amenable to main memory. In this paper we thus explore the opportunity to use light-weight machine learning models to accelerate queries on spatial indexes. We do so by exploring the potential of using interpolation and similar techniques on the R-tree, arguably the most broadly used spatial index. As we show in our experimental analysis, the query execution time can be reduced by up to 60% while simultaneously shrinking the index's memory footprint by over 90%

中文翻译：

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空间索引结构中的 Hands-off 模型集成

空间索引对于分析越来越多的空间数据至关重要，例如通过物联网应用程序生成的空间数据。近几十年来开发的大量索引主要针对磁盘进行了优化。然而，即使在商用机器上，随着内存量的增加，将它们移动到主内存也是一种选择。这样做为使用仅适用于主内存的其他优化提供了机会。因此，在本文中，我们探索了使用轻量级机器学习模型来加速空间索引查询的机会。我们通过探索在 R 树（可以说是最广泛使用的空间索引）上使用插值和类似技术的潜力来做到这一点。正如我们在实验分析中所展示的，