Budgeted Spatial Data Acquisition: When Coverage and Connectivity Matter

TL;DR

This paper introduces a novel problem formulation for budgeted spatial data acquisition emphasizing coverage and connectivity, proposing approximation algorithms with theoretical guarantees and validating them through real-world experiments.

Contribution

First formulation of Budgeted Maximum Coverage with Connectivity Constraint for spatial data acquisition, along with approximate algorithms and efficiency strategies.

Findings

Algorithms achieve high coverage within budget constraints.

Theoretical guarantees ensure solution quality.

Experimental results confirm efficiency and effectiveness.

Abstract

Data is undoubtedly becoming a commodity like oil, land, and labor in the 21st century. Although there have been many successful marketplaces for data trading, the existing data marketplaces lack consideration of the case where buyers want to acquire a collection of datasets (instead of one), and the overall spatial coverage and connectivity matter. In this paper, we take the first attempt to formulate this problem as Budgeted Maximum Coverage with Connectivity Constraint (BMCC), which aims to acquire a dataset collection with the maximum spatial coverage under a limited budget while maintaining spatial connectivity. To solve the problem, we propose two approximate algorithms with detailed theoretical guarantees and time complexity analysis, followed by two acceleration strategies to further improve the efficiency of the algorithm. Experiments are conducted on five real-world spatial dataset collections to verify the efficiency and effectiveness of our algorithms.