GP-Tree: An in-memory spatial index combining adaptive grid cells with a prefix tree for efficient spatial querying
🤖 AI Summary
This work proposes GP-Tree, an in-memory spatial index that integrates adaptive fine-grained grid approximation with a trie-based structure to overcome the limitations of traditional spatial indexes—such as STR-trees and quad-trees—which rely on coarse minimum bounding rectangles (MBRs) and consequently suffer from low filtering precision and poor query efficiency. GP-Tree accurately approximates complex spatial object shapes using grids and leverages shared-prefix compression to reduce storage overhead and accelerate traversal. Combined with tree pruning and node optimization strategies, it efficiently supports range, distance, and k-nearest neighbor queries. Experimental evaluation on real-world datasets demonstrates that GP-Tree achieves up to an order-of-magnitude improvement in query performance compared to conventional spatial indexing methods.
Application Category
📝 Abstract
Efficient spatial indexing is crucial for processing large-scale spatial data. Traditional spatial indexes, such as STR-Tree and Quad-Tree, organize spatial objects based on coarse approximations, such as their minimum bounding rectangles (MBRs). However, this coarse representation is inadequate for complex spatial objects (e.g., district boundaries and trajectories), limiting filtering accuracy and query performance of spatial indexes. To address these limitations, we propose GP-Tree, a fine-grained spatial index that organizes approximated grid cells of spatial objects into a prefix tree structure. GP-Tree enhances filtering ability by replacing coarse MBRs with fine-grained cell-based approximations of spatial objects. The prefix tree structure optimizes data organization and query efficiency by leveraging the shared prefixes in the hierarchical grid cell encodings between parent and child cells. Additionally, we introduce optimization strategies, including tree pruning and node optimization, to reduce search paths and memory consumption, further enhancing GP-Tree's performance. Finally, we implement a variety of spatial query operations on GP-Tree, including range queries, distance queries, and k-nearest neighbor queries. Extensive experiments on real-world datasets demonstrate that GP-Tree significantly outperforms traditional spatial indexes, achieving up to an order-of-magnitude improvement in query efficiency.
Problem
Research questions and friction points this paper is trying to address.
spatial indexing
complex spatial objects
filtering accuracy
query performance
minimum bounding rectangles
Innovation
Methods, ideas, or system contributions that make the work stand out.
spatial indexing
grid-based approximation
prefix tree
fine-grained representation
query optimization
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