A distributed framework for large-scale semantic trajectory similarity join

The similarity join is a common yet expensive operator for large-scale semantic trajectories analytics. In this paper, we propose DFST, an efficient framework for semantic trajectory similarity join in distributed systems. We devise ITS index and summary index, which consider textual, temporal, and spatial domains, and theoretically demonstrate that they can effectively prune pairs of dissimilar trajectories. Moreover, DFST can support most existing similarity functions to quantify the spatial similarity between semantic trajectories. We have conducted extensive experiments on real world datasets, and experimental results show that DFST achieves a 13.6% improvement of join performance compared to existing semantic trajectory similarity join methods.

Automated summary

In this paper, we propose DFST, an efficient framework for semantic trajectory similarity join in distributed systems, which achieves a 13.6% improvement of join performance compared to existing methods. DFST utilizes ITS index and summary index to prune dissimilar trajectory pairs and supports most existing similarity functions to quantify spatial similarity. Experimental results on real world datasets demonstrate the effectiveness of DFST.