Efficient processing of raster and vector data

TL;DR

This paper introduces a new framework for efficiently processing raster and vector spatial data, featuring algorithms that operate directly on compressed data to improve speed and memory usage.

Contribution

The work presents novel algorithms for raster-vector spatial joins and object retrieval that operate directly on compressed data, enhancing efficiency.

Findings

Achieved better space/time trade-offs compared to baselines.

Algorithms operate directly on compressed data, reducing memory usage.

Experimental results show improved performance in processing spatial data.

Abstract

In this work, we propose a framework to store and manage spatial data, which includes new efficient algorithms to perform operations accepting as input a raster dataset and a vector dataset. More concretely, we present algorithms for solving a spatial join between a raster and a vector dataset imposing a restriction on the values of the cells of the raster; and an algorithm for retrieving K objects of a vector dataset that overlap cells of a raster dataset, such that the K objects are those overlapping the highest (or lowest) cell values among all objects. The raster data is stored using a compact data structure, which can directly manipulate compressed data without the need for prior decompression. This leads to better running times and lower memory consumption. In our experimental evaluation comparing our solution to other baselines, we obtain the best space/time trade-offs.