论文标题
空间索引结构中的移动模型集成
Hands-off Model Integration in Spatial Index Structures
论文作者
论文摘要
空间索引对于分析越来越多的空间数据（例如通过IoT应用程序生成的空间数据）至关重要。近几十年来开发的大量索引主要是针对磁盘进行了优化的。但是，即使在商品机上，随着内存量的增加，将它们移至主内存是一个选择。这样做开启了使用仅适用于主内存的其他优化的机会。因此，在本文中，我们探索了使用轻型机器学习模型来加速空间索引的查询的机会。我们通过探索在R-Tree上使用插值和类似技术的潜力，这可以说是最广泛使用的空间指数。正如我们在实验分析中所显示的那样，查询执行时间可以减少60％，同时缩小指数的内存足迹超过90％
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%
