Generative_Techniques_Spatial_Temporal_Data_Mining.pptx
- 1. Generative Techniques in Spatial-Temporal Data Mining Exploring Frameworks, Challenges, and Applications
- 2. Introduction to Spatial-Temporal Data ā¢ Spatial-temporal data captures patterns evolving across space and time. ā¢ Applications: Transportation, healthcare, urban planning, and climate science. ā¢ Challenges: Correlations between spatial and temporal aspects, and data heterogeneity.
- 3. Challenges in Spatial-Temporal Data Mining 1. Correlations: - Temporal: Relationships between sequential events. - Spatial: Regional patterns and proximities. - Combined: Complex dependencies. 2. Heterogeneity: - Variability across regions and time periods. - Imbalanced data leading to biases. - Generalization issues in models.
- 4. Data Types in Spatial-Temporal Analysis 1. Event Data: - Represents discrete occurrences like crimes or sales. 2. Trajectory Data: - Captures movement paths (e.g., taxi routes). 3. Point Data: - Measurements at specific locations (e.g., weather stations). 4. Raster Data: - Grid-based observations like satellite imagery.
- 5. Generative Techniques Overview 1. Large Language Models (LLMs): - Utilize transformer-based architectures. 2. Diffusion Models: - Simulate dynamic systems and model uncertainty. 3. Self-Supervised Learning (SSL): - Extracts meaningful representations without labeled data. 4. Seq2Seq Frameworks: - Transforms input sequences into outputs with attention mechanisms.
- 6. Framework for Spatial-Temporal Data Mining 1. Data Collection: - Sources: GPS devices, weather stations, and satellites. 2. Preprocessing: - Cleaning and transforming data for analysis. 3. Generative Modeling: - Applying advanced techniques like LLMs and diffusion models.
- 7. Applications of Generative Techniques 1. Representation Learning: - Generates embeddings for tasks like mobility analysis. 2. Forecasting: - Predicts future states like traffic flow and weather. 3. Recommendations: - Personalized suggestions based on spatial-temporal patterns. 4. Clustering: - Groups data to reveal inherent structures.
- 8. Future Research Directions 1. Addressing Dataset Biases: - Ensuring fairness in imbalanced datasets. 2. Scaling Foundation Models: - Developing large-scale, high-quality datasets. 3. Enhancing Generalization: - Building models adaptable to diverse contexts. 4. Leveraging External Knowledge: - Incorporating knowledge graphs for deeper insights.
- 9. Conclusion ā¢ Generative techniques transform spatial-temporal data mining. ā¢ Overcome traditional model limitations. ā¢ Unlock new opportunities across domains with advanced frameworks.