Adaptive row major order: a new space filling curve for efficient spatial join processing in the transform space
A transform-space index indexes spatial objects represented as points in the transform space. An advantage of a transform-space index is that optimization of spatial join algorithms using these indexes can be more formal. The authors earlier proposed the Transform-Based Spatial Join algorithm that joins two transform-space indexes. It renders global optimization easy with little overhead by utilizing the characteristics of the transform space. In particular, it allows us to globally determine the order of accessing disk pages, which makes a significant impact on the performance of joins. For this purpose, we use various space filling curves. In this paper, we propose a new space filling curve called the adaptive row major order (ARM order). The ARM order adaptively controls the order of accessing pages and significantly reduces the one-pass buffer size (the minimum buffer size required for guaranteeing one disk access per page) and the number of disk accesses for a given buffer size. Through analysis and experiments, we verify excellence of the ARM order when used with the Transform-Based Spatial Join. The Transform-Based Spatial Join with the ARM order always outperforms those with other conventional space filling curves in terms of both measures used: the one-pass buffer size and the number of disk accesses. Specifically, it reduces the one-pass buffer size by up to 25.9 times and the number of disk accesses by up to 2.11 times. We conclude that we achieve these results mainly due to global optimization of the order of accessing disk pages using an adaptive space filling curve.