Two-layer Space-oriented Partitioning for Non-point Data

TL;DR

This paper introduces a two-layer space-oriented partitioning method for non-point spatial data that enhances index performance, reduces duplicate results, and accelerates spatial joins, especially in distributed environments.

Contribution

The paper presents a novel secondary partitioning technique for space-oriented indices that improves efficiency and is suitable for distributed and parallel processing.

Findings

Reduces duplicate result generation in spatial queries

Improves spatial join performance by around 50%

Effective on real datasets compared to existing methods

Abstract

Non-point spatial objects (e.g., polygons, linestrings, etc.) are ubiquitous. We study the problem of indexing non-point objects in memory for range queries and spatial intersection joins. We propose a secondary partitioning technique for space-oriented partitioning indices (e.g., grids), which improves their performance significantly, by avoiding the generation and elimination of duplicate results. Our approach is easy to implement and can be used by any space-partitioning index to significantly reduce the cost of range queries and intersection joins. In addition, the secondary partitions can be processed independently, which makes our method appropriate for distributed and parallel indexing. Experiments on real datasets confirm the advantage of our approach against alternative duplicate elimination techniques and data-oriented state-of-the-art spatial indices. We also show that our partitioning technique, paired with optimized partition-to-partition join algorithms, typically reduces the cost of spatial joins by around 50%.