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<p>The spatial keyword skyline query effectively addresses user preferences, particularly for location-based service applications. However, existing approaches primarily focus on single-object queries and remain unsuitable for group-based computations. To fulfill the requirements for group-oriented queries, a novel spatial keyword group-based skyline query was proposed. Firstly, the spatial-keyword coefficient was introduced to transforms spatial coordinates and keyword semantics into numerical values. Then, a spatial-keyword grid index was designed to efficiently locate query positions and perform keyword-based object filtering. Building upon this foundation, pruning theorems and group classification principles were proved, followed by the development of a spatial keyword group-based skyline (SKG-skyline) algorithm. Experimental evaluations on real-world datasets demonstrate that our algorithm outperforms existing methods in computational efficiency.</p>

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A novel multi-objective SKG-skyline algorithm integrating spatial keyword data transformation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Xiaowei Cui,
  • Leigang Dong,
  • Guohao Sun,
  • Yuanjun Huang,
  • Yixuan Chai,
  • Quan Yu

摘要

The spatial keyword skyline query effectively addresses user preferences, particularly for location-based service applications. However, existing approaches primarily focus on single-object queries and remain unsuitable for group-based computations. To fulfill the requirements for group-oriented queries, a novel spatial keyword group-based skyline query was proposed. Firstly, the spatial-keyword coefficient was introduced to transforms spatial coordinates and keyword semantics into numerical values. Then, a spatial-keyword grid index was designed to efficiently locate query positions and perform keyword-based object filtering. Building upon this foundation, pruning theorems and group classification principles were proved, followed by the development of a spatial keyword group-based skyline (SKG-skyline) algorithm. Experimental evaluations on real-world datasets demonstrate that our algorithm outperforms existing methods in computational efficiency.