5 papers
FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning
Zhihao Zeng, Ziquan Fang, Wei Shao +2
Trajectory data, which capture the movement patterns of people and vehicles over time and space, are crucial for applications like traffic optimization and urban planning. However,…
Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
Yuting Huang, Ziquan Fang, Zhihao Zeng +2
Spatio-temporal prediction plays a crucial role in intelligent transportation, weather forecasting, and urban planning. While integrating multi-modal data has shown potential for e…
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective
Zhihao Zeng, Ziquan Fang, Yuting Huang +2
Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer…
Snoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns
Yuxiang Guo, Yuren Mao, Zhonghao Hu +2
Semantic join discovery, which aims to find columns in a table repository with high semantic joinabilities to a query column, is crucial for dataset discovery. Existing methods can…
DIMS: Distributed Index for Similarity Search in Metric Spaces
Yifan Zhu, Chengyang Luo, Tang Qian +3
Similarity search finds objects that are similar to a given query object based on a similarity metric. As the amount and variety of data continue to grow, similarity search in metr…