Parallel Dynamic Spatial Indexes
🤖 AI Summary
This work addresses the inefficiency of parallel batch updates in highly dynamic spatial data by systematically introducing two novel parallel spatial index structures: the P-Orth tree and the SPaC-tree, built upon the Orth-tree and R-tree/BVH frameworks, respectively. By designing efficient parallel batch update algorithms and index maintenance strategies, the proposed methods significantly outperform existing parallel kd-trees and Orth-trees across diverse dynamic workloads. They achieve substantially lower batch update latency while maintaining comparable or even superior query performance. This study establishes a high-performance, scalable indexing foundation for managing dynamic spatial data.
Application Category
📝 Abstract
Maintaining spatial data (points in two or three dimensions) is crucial and has a wide range of applications, such as graphics, GIS, and robotics. To handle spatial data, many data structures, called spatial indexes, have been proposed, e.g. kd-trees, oct/quadtrees (also called Orth-trees), R-trees, and bounding volume hierarchies (BVHs). In real-world applications, spatial datasets tend to be highly dynamic, requiring batch updates of points with low latency. This calls for efficient parallel batch updates on spatial indexes. Unfortunately, there is very little work that achieves this. In this paper, we systematically study parallel spatial indexes, with a special focus on achieving high-performance update performance for highly dynamic workloads. We select two types of spatial indexes that are considered optimized for low-latency updates: Orth-tree and R-tree/BVH. We propose two data structures: the P-Orth tree, a parallel Orth-tree, and the SPaC-tree family, a parallel R-tree/BVH. Both the P-Orth tree and the SPaC-tree deliver superior performance in batch updates compared to existing parallel kd-trees and Orth-trees, while preserving better or competitive query performance relative to their corresponding Orth-tree and R-tree counterparts. We also present comprehensive experiments comparing the performance of various parallel spatial indexes and share our findings at the end of the paper.
Problem
Research questions and friction points this paper is trying to address.
parallel spatial indexes
dynamic spatial data
batch updates
low-latency updates
spatial indexing
Innovation
Methods, ideas, or system contributions that make the work stand out.
parallel spatial indexes
batch updates
P-Orth tree
SPaC-tree
dynamic spatial data
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