A Survey of Generative Techniques for Spatial-Temporal Data Mining
<|reference_start|>A Survey of Generative Techniques for Spatial-Temporal Data Mining: 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.<|reference_end|>
@article{zhang2024a,
    title={A Survey of Generative Techniques for Spatial-Temporal Data Mining},
    author={Qianru Zhang, Haixin Wang, Cheng Long, Liangcai Su, Xingwei He,
  Jianlong Chang, Tailin Wu, Hongzhi Yin, Siu-Ming Yiu, Qi Tian and Christian
  S. Jensen},
    journal={arXiv preprint arXiv:2405.09592},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09592},
    primaryClass={cs.LG cs.AI cs.CE}
}