Continuous trajectory similarity search with result diversification.

With the continued increasing availability of spatio-temporal data from GPS-equipped devices, online map services, and a variety of location-based social media, trajectory similarity search has become a fundamental operation in location-based data analytics. It is of great importance to enable realtime search of trajectories that satisfy users' personalized requirements. For the purpose, we study the Diversified Continuous Trajectory Similarity Search (DCTSS) problem. The DCTSS problem aims to process a large number of Continuous Location Set (CLS) queries over a stream of trajectory data while taking result diversity of each query into consideration. To answer the DCTSS problem, we develop a Diversity-Aware Trajectory Publish/Subscribe (DAT-PS) framework, which takes both trajectory data streams and CLS queries as input and performs query-trajectory matching between CLS queries in the query collection and trajectories over the trajectory data stream. Our experimental results on two real-life datasets show that our proposed DAT-PS framework is capable of demonstrating substantial superiority regarding both efficiency and scalability compared against baselines. (c) 2023 Elsevier B.V. All rights reserved.