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<p>With the continued digitization of social and industrial processes, diverse and increasingly massive volumes of spatial-temporal data are being accumulated， which enable the use of data mining to fuel important applications. Thus, neural network-based techniques have been developed to capture spatial and temporal dependencies in spatial-temporal data. Most recently, the advances in <i>generative techniques</i>, including large language models, masked autoencoders, sequence-to-sequence models, diffusion models, and others, have led to their increased use in spatial-temporal data mining, thereby driving new advances in spatial-temporal data mining. This paper describes the general types of spatial-temporal data, all kinds of spatial-temporal data instances, and <i>generative techniques</i>, and it proposes a spatial-temporal data mining pipeline. Further, it delivers a structured overview of <i>generative techniques</i> for spatial-temporal data mining, grounded in a novel taxonomy. Moreover, by outlining promising research avenues enabled by <i>generative techniques</i>, the paper seeks to accelerate advances in spatial-temporal data mining.</p>

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A Survey of Generative Techniques for Spatial-Temporal Data Mining

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月22日

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  • Qianru Zhang,
  • Haixin Wang,
  • Honggang Wen,
  • Cheng Long,
  • Liangcai Su,
  • Xingwei He,
  • Tailin Wu,
  • Christian S. Jensen,
  • Siu-Ming Yiu,
  • Hongzhi Yin

摘要

With the continued digitization of social and industrial processes, diverse and increasingly massive volumes of spatial-temporal data are being accumulated， which enable the use of data mining to fuel important applications. Thus, neural network-based techniques have been developed to capture spatial and temporal dependencies in spatial-temporal data. Most recently, the advances in generative techniques, including large language models, masked autoencoders, sequence-to-sequence models, diffusion models, and others, have led to their increased use in spatial-temporal data mining, thereby driving new advances in spatial-temporal data mining. This paper describes the general types of spatial-temporal data, all kinds of spatial-temporal data instances, and generative techniques, and it proposes a spatial-temporal data mining pipeline. Further, it delivers a structured overview of generative techniques for spatial-temporal data mining, grounded in a novel taxonomy. Moreover, by outlining promising research avenues enabled by generative techniques, the paper seeks to accelerate advances in spatial-temporal data mining.