Similarity Search on Semantic Trajectories Using Text Processing

Abstract

The use of location-based sensors has increased exponentially. Tracking moving objects has become increasingly common, consolidating a new field of research that focuses on trajectory data management. Such trajectories may be semantically enriched using sensors and social media. This enables a detailed analysis of trajectory behavior patterns. One of the problems in this field is the search for a semantic trajectory database that is flexible and adaptable; flexibility in the sense of retrieving trajectories that are closest to the user’s query and not just based on exact matching. Adaptability refers to adjusting to different types of semantic trajectories. This article proposes a new approach for representing and querying semantic trajectories based on text-processing techniques. Furthermore, we describe a framework, called SETHE (SEmantic Trajectory HuntEr), that performs similarity queries on semantically enriched trajectory databases. SETHE can be adapted according to the aspect types posed in user queries. We also presented an evaluation of the proposed framework using a real dataset, and compare our results with those of state-of-the-art approaches.