Nearly Optimal Planar k Nearest Neighbors Queries under General Distance Functions

TL;DR

This paper introduces nearly optimal static and dynamic data structures for planar k nearest neighbors queries under general distance functions, improving space complexity while maintaining optimal query times.

Contribution

It develops a new random sampling technique and shallow cuttings for general distance functions, achieving near-linear space and optimal query time for kNN queries in the plane.

Findings

Achieves O(n log log n) space with O(log n + k) query time for static data structures.

Reduces dynamic structure space from O(n log^3 n) to O(n log n).

Introduces a novel random sampling technique for geometric algorithms.

Abstract

We study the k nearest neighbors problem in the plane for general, convex, pairwise disjoint sites of constant description complexity such as line segments, disks, and quadrilaterals and with respect to a general family of distance functions including the L_p-norms and additively weighted Euclidean distances. For point sites in the Euclidean metric, after four decades of effort, an optimal data structure has recently been developed with O( n ) space, O( log n + k ) query time, and O( n log n ) preprocessing time. We develop a static data structure for the general setting with nearly optimal O( n log log n ) space, the optimal O( log n + k ) query time, and expected O( n polylog n ) preprocessing time. The O( n log log n ) space approaches the linear space, whose achievability is still unknown with the optimal query time, and improves the so far best O( n ( log^2 n )( log log n )^2 ) space of Bohler et al.'s work. Our dynamic version (that allows insertions and deletions of sites) also reduces the space of Kaplan et al.'s work from O( n log^3 n ) to O( n log n ). To obtain these progresses, we devise shallow cuttings of linear size for general distance functions. Shallow cuttings are a key technique to deal with the k nearest neighbors problem for point sites in the Euclidean metric. Agarwal et al. already designed linear-size shallow cuttings for general distance functions, but their shallow cuttings could not be applied to the k nearest neighbors problem. Recently, Kaplan et al. constructed shallow cuttings that are feasible for the k nearest neighbors problem, while the size of their shallow cuttings has an extra double logarithmic factor. Our innovation is a new random sampling technique for the analysis of geometric structures. Since our new technique provides a new way to develop and analyze geometric algorithms, we believe it is of independent interest.