Adaptive Geospatial Joins for Modern Hardware

TL;DR

This paper introduces an adaptive geospatial join method optimized for modern hardware, enabling real-time mapping of streaming points to static polygons with significantly improved speed and efficiency.

Contribution

The authors develop a novel adaptive geospatial join algorithm that employs hierarchical grids and SIMD optimization, achieving up to 100x faster performance than prior techniques.

Findings

Up to two orders of magnitude faster than existing methods.

Effective approximation guarantees user-defined precision.

Optimized for modern SIMD hardware architectures.

Abstract

Geospatial joins are a core building block of connected mobility applications. An especially challenging problem are joins between streaming points and static polygons. Since points are not known beforehand, they cannot be indexed. Nevertheless, points need to be mapped to polygons with low latencies to enable real-time feedback. We present an adaptive geospatial join that uses true hit filtering to avoid expensive geometric computations in most cases. Our technique uses a quadtree-based hierarchical grid to approximate polygons and stores these approximations in a specialized radix tree. We emphasize on an approximate version of our algorithm that guarantees a user-defined precision. The exact version of our algorithm can adapt to the expected point distribution by refining the index. We optimized our implementation for modern hardware architectures with wide SIMD vector processing units, including Intel's brand new Knights Landing. Overall, our approach can perform up to two orders of magnitude faster than existing techniques.