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<p>Data is undoubtedly becoming a commodity like oil, land, and labour 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 make the first attempt to formulate this problem as Budgeted Maximum Coverage with Connectivity Constraint (<b>BMCC</b>), 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.</p>

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Budgeted spatial data acquisition: when coverage and connectivity matter

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Wenzhe Yang,
  • Shixun Huang,
  • Sheng Wang,
  • Zhiyong Peng

摘要

Data is undoubtedly becoming a commodity like oil, land, and labour 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 make 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.