REST: An Efficient Distributed Framework for Real-time Semantic Trajectory Join

Abstract

Abstract

Trajectory similarity join is a fundamental problem in spatiotemporal data management, aimed at identifying pairs of similar trajectories within two trajectory datasets. With the growth of mobile devices, streaming trajectory data is continuously uploaded, enabling real-time trajectory stream analysis for location-based services. In this paper, we investigate the problem of Online Semantic Trajectory Similarity Join (OSTS-J). Given two streaming semantic trajectory datasets, a similarity threshold $$\theta$$ , and a time window $$L$$ , OSTS-J returns all trajectory pairs within the current time window $$L$$ that meet the threshold $$\theta$$ constraint. This problem has significant applications in real-time route planning and travel itinerary suggestions. To efficiently address the OSTS-J problem over large-scale semantic trajectory datasets in a distributed framework, we propose a two-stage algorithm, REST, based on the principle of “defer computation until necessary." In the first stage, we employ the AIR-index alongside pruning strategies to avoid unnecessary trajectory pair computations at the global level, ensuring effective load balancing. In the second stage, we use an enhanced version of incremental computation, Inc++, to further defer unnecessary refinement of trajectory pairs at the local level. We evaluate the effectiveness, scalability, and load-balancing performance of our framework through experiments on four real-world datasets. The results demonstrate that our algorithm (i) outperforms state-of-the-art methods by achieving approximately a 97% reduction in latency and a 35.6 $$\times$$ increase in throughput under optimal conditions, and (ii) enhances load balancing performance and system stability.