A Scalable Algorithm for Maximizing Range Sum in Spatial Databases

TL;DR

This paper introduces a scalable external-memory algorithm for the MaxRS problem in spatial databases, significantly improving efficiency over existing in-memory methods and providing an approximation for the MaxCRS variant.

Contribution

It presents the first scalable external-memory algorithm for MaxRS, proves its optimality, and offers an approximation algorithm for MaxCRS with proven bounds.

Findings

ExactMaxRS is two orders of magnitude faster than existing methods.

ExactMaxRS is proven to be optimal in I/O complexity.

ApproxMaxCRS performs better in practice than its theoretical bound.

Abstract

This paper investigates the MaxRS problem in spatial databases. Given a set O of weighted points and a rectangular region r of a given size, the goal of the MaxRS problem is to find a location of r such that the sum of the weights of all the points covered by r is maximized. This problem is useful in many location-based applications such as finding the best place for a new franchise store with a limited delivery range and finding the most attractive place for a tourist with a limited reachable range. However, the problem has been studied mainly in theory, particularly, in computational geometry. The existing algorithms from the computational geometry community are in-memory algorithms which do not guarantee the scalability. In this paper, we propose a scalable external-memory algorithm (ExactMaxRS) for the MaxRS problem, which is optimal in terms of the I/O complexity. Furthermore, we propose an approximation algorithm (ApproxMaxCRS) for the MaxCRS problem that is a circle version of the MaxRS problem. We prove the correctness and optimality of the ExactMaxRS algorithm along with the approximation bound of the ApproxMaxCRS algorithm. From extensive experimental results, we show that the ExactMaxRS algorithm is two orders of magnitude faster than methods adapted from existing algorithms, and the approximation bound in practice is much better than the theoretical bound of the ApproxMaxCRS algorithm.