This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other non-generative techniques, researchers have explored their application in capturing temporal and spatial dependencies within spatial-temporal data. However, the emergence of generative techniques such as LLMs, SSL, Seq2Seq and diffusion models has opened up new possibilities for enhancing spatial-temporal data mining further. The paper provides a comprehensive analysis of generative technique-based spatial-temporal methods and introduces a standardized framework specifically designed for the spatial-temporal data mining pipeline. By offering a detailed review and a novel taxonomy of spatial-temporal methodology utilizing generative techniques, the paper enables a deeper understanding of the various techniques employed in this field. Furthermore, the paper highlights promising future research directions, urging researchers to delve deeper into spatial-temporal data mining. It emphasizes the need to explore untapped opportunities and push the boundaries of knowledge to unlock new insights and improve the effectiveness and efficiency of spatial-temporal data mining. By integrating generative techniques and providing a standardized framework, the paper contributes to advancing the field and encourages researchers to explore the vast potential of generative techniques in spatial-temporal data mining.

翻译：本文聚焦于生成技术与时空数据挖掘的融合，考虑到时空数据的显著增长及其多样性。随着RNN、CNN等非生成技术的发展，研究人员已探索其在捕捉时空数据中时间与空间依赖关系中的应用。然而，LLM（大型语言模型）、SSL（自监督学习）、Seq2Seq（序列到序列模型）及扩散模型等生成技术的出现，为进一步提升时空数据挖掘开辟了新可能。本文对基于生成技术的时空方法进行了全面分析，并提出了专为时空数据挖掘流水线设计的标准化框架。通过详细综述时空方法学并创新性地运用生成技术对其进行分类，本文深化了对该领域所采用多种技术的理解。此外，本文指出了有前景的未来研究方向，呼吁研究人员深入探索时空数据挖掘，强调发掘未开发机遇、突破知识边界以解锁新洞察，进而提升时空数据挖掘的有效性与效率。通过整合生成技术并提供标准化框架，本文推动了该领域的发展，并鼓励研究人员探索生成技术在时空数据挖掘中的巨大潜力。