SeeD: Online similarity-preserving pattern discovery for streaming trajectories

The rapid accumulation of fresh trajectory data has fueled a growing interest in the analysis of such data. There has been a notable economic and social value attributed to effectively uncovering mobility behaviors within rich, streaming trajectory data for applications like urban planning, marketing and intelligence. Despite extensive research on pattern discovery, existing methods often confine themselves to fixed patterns, neglecting the potential synergy between pattern discovery and similarity queries. This synergy can be bidirectional: similarity results could be the foundation of pattern discovery, while pattern discovery can accelerate the similarity queries. To bridge this gap, we propose the Online Similarity-preserving Trajectory Pattern Discovery, called SeeD. This framework consists of three core modules: (1) The composite windowing strategy, which extracts multi-scale trajectory information and maintains correlation patterns, ensuring data relevance across various scales. (2) The Clustering-based Similarity Query (CSQ) module, which accelerates similarity computation based on pattern discovery results, thus improving query efficiency. (3) The Evolution Detection and Analysis (EDA) module, which enhances overall performance by analyzing pattern evolution, providing insights into dynamic changes within trajectory data. Extensive experimental results conducted on well-established datasets unequivocally demonstrate the effectiveness of SeeD, indicating its potential to revolutionize the field by offering a robust solution for pattern discovery.