Ball*-tree: Efficient spatial indexing for constrained nearest-neighbor search in metric spaces

TL;DR

Ball*-tree is an improved spatial indexing structure that enhances the efficiency of nearest-neighbor and range searches in metric spaces by considering data distribution during partitioning.

Contribution

This paper introduces Ball*-tree, a novel variant of Ball-tree with a modified partitioning algorithm for better spatial query performance.

Findings

Ball*-tree is 39%-57% faster than the original Ball-tree.

It improves KNN and range query performance for spatial data.

The new algorithm effectively utilizes data distribution for partitioning.

Abstract

Emerging location-based systems and data analysis frameworks requires efficient management of spatial data for approximate and exact search. Exact similarity search can be done using space partitioning data structures, such as Kd-tree, R*-tree, and Ball-tree. In this paper, we focus on Ball-tree, an efficient search tree that is specific for spatial queries which use euclidean distance. Each node of a Ball-tree defines a ball, i.e. a hypersphere that contains a subset of the points to be searched. In this paper, we propose Ball*-tree, an improved Ball-tree that is more efficient for spatial queries. Ball*-tree enjoys a modified space partitioning algorithm that considers the distribution of the data points in order to find an efficient splitting hyperplane. Also, we propose a new algorithm for KNN queries with restricted range using Ball*-tree, which performs better than both KNN and range search for such queries. Results show that Ball*-tree performs 39%-57% faster than the original Ball-tree algorithm.