Aggregated 2D Range Queries on Clustered Points

TL;DR

This paper presents a space-efficient technique for fast aggregated 2D range queries on clustered data points, significantly improving query speed in applications like GIS and OLAP.

Contribution

The paper introduces a novel representation method for 2D grids that supports efficient aggregated range queries on clustered data with minimal space overhead.

Findings

Query speed improved by over an order of magnitude

Supports ranked and counting range queries effectively

Requires little additional space for clustered datasets

Abstract

Efficient processing of aggregated range queries on two-dimensional grids is a common requirement in information retrieval and data mining systems, for example in Geographic Information Systems and OLAP cubes. We introduce a technique to represent grids supporting aggregated range queries that requires little space when the data points in the grid are clustered, which is common in practice. We show how this general technique can be used to support two important types of aggregated queries, which are ranked range queries and counting range queries. Our experimental evaluation shows that this technique can speed up aggregated queries up to more than an order of magnitude, with a small space overhead.