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<p>Trajectory data are nowadays ubiquitous. This paper investigates the problem of durable nearest-neighbor query processing over trajectories. Durable queries are useful in various practical applications such as disaster management, urban planning, self-driving, etc. In particular, durable nearest-neighbor queries may find trajectory data clusters with natural spatiotemporal distributions and dynamics, which are beneficial to trajectory data generation for advanced data analysis. We develop an efficient durable nearest-neighbor query processing algorithm following the filter-and-refine paradigm. Specifically, by properly formalizing the problem, we develop tight distance bounds and tree-based data indexes, which lead to efficient pruning during the search process. Experiments on real and synthetic datasets demonstrate the efficiency and scalability of our methods.</p>

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Efficient nearest-neighbor search on moving objects with durability constraints

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • Wei Zhao,
  • Hao Wang,
  • Wei Yu

摘要

Trajectory data are nowadays ubiquitous. This paper investigates the problem of durable nearest-neighbor query processing over trajectories. Durable queries are useful in various practical applications such as disaster management, urban planning, self-driving, etc. In particular, durable nearest-neighbor queries may find trajectory data clusters with natural spatiotemporal distributions and dynamics, which are beneficial to trajectory data generation for advanced data analysis. We develop an efficient durable nearest-neighbor query processing algorithm following the filter-and-refine paradigm. Specifically, by properly formalizing the problem, we develop tight distance bounds and tree-based data indexes, which lead to efficient pruning during the search process. Experiments on real and synthetic datasets demonstrate the efficiency and scalability of our methods.