On Top-$k$ Weighted SUM Aggregate Nearest and Farthest Neighbors in the $L_1$ Plane

TL;DR

This paper introduces efficient data structures for top-$k$ aggregate nearest and farthest neighbor queries under the $L_1$ metric, improving query times and preprocessing costs over previous methods.

Contribution

It presents new data structures with optimized preprocessing and query times for top-$k$ aggregate nearest and farthest neighbor searches in the plane.

Findings

Achieves $O(n ext{log} n ext{log} ext{log} n)$ preprocessing time and space.

Answers top-$k$ queries in $O(m ext{log} m+(k+m) ext{log}^2 n)$ time.

Provides specialized solutions for the 1D case with $O(n)$ space and $O(n ext{log} n)$ preprocessing.

Abstract

In this paper, we study top-$k$ aggregate (or group) nearest neighbor queries using the weighted SUM operator under the $L_1$ metric in the plane. Given a set $P$ of $n$ points, for any query consisting of a set $Q$ of $m$ weighted points and an integer $k$, $ 1 \le k \le n$, the top-$k$ aggregate nearest neighbor query asks for the $k$ points of $P$ whose aggregate distances to $Q$ are the smallest, where the aggregate distance of each point $p$ of $P$ to $Q$ is the sum of the weighted distances from $p$ to all points of $Q$. We build an $O(n\log n\log\log n)$-size data structure in $O(n\log n \log\log n)$ time, such that each top-$k$ query can be answered in $O(m\log m+(k+m)\log^2 n)$ time. We also obtain other results with trade-off between preprocessing and query. Even for the special case where $k=1$, our results are better than the previously best method (in PODS 2012), which requires $O(n\log^2 n)$ preprocessing time, $O(n\log^2 n)$ space, and $O(m^2\log^3 n)$ query time. In addition, for the one-dimensional version of this problem, our approach can build an $O(n)$-size data structure in $O(n\log n)$ time that can support $O(\min\{k,\log m\}\cdot m+k+\log n)$ time queries. Further, we extend our techniques to the top-$k$ aggregate farthest neighbor queries, with the same bounds.