A Survey of Generative Techniques for Spatial-Temporal Data Mining

TL;DR

The paper analyzes how generative techniques—specifically LLMs, diffusion models, SSL, and Seq2Seq—can advance spatial-temporal data mining. It introduces a standardized framework and a novel taxonomy to structure ST methods, covering representation learning, forecasting, recommendation, and clustering. Key contributions include a contemporary synthesis of generative ST techniques and a pipeline guiding method selection and application across ST tasks. The work highlights future directions such as scalable foundation models and external knowledge integration, underscoring potential gains in zero-shot forecasting, uncertainty handling, and cross-domain generalization for spatial-temporal analytics.

Abstract

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.

Figures (7)

• Figure 1: Examples of existing studies via generative techniques.
• Figure 2: Example of Event data and trajectory data
• Figure 3: Example of Point data at different timestamps
• Figure 4: Example of Raster data at regular time and place and irregular time and space
• Figure 5: Mapping between data type and data instance