A model for enriching trajectories with semantic geographical information

TLDR

The growing prevalence of moving object data creates a need for efficient analysis, but trajectory data lack semantic information, making analysis computationally expensive and user‑complex, and enriching trajectories with semantic geographical information can simplify queries, analysis, and mining. The paper proposes a preprocessing model that adds semantic information to trajectories to facilitate analysis across various application domains. The generic model represents key trajectory parts relevant to the application and includes an algorithm that identifies these parts, enabling efficient semantic analysis. The algorithm reduces query complexity for semantic trajectory analysis, demonstrating significant efficiency gains.

Abstract

The collection of moving object data is becoming more and more common, and therefore there is an increasing need for the efficient analysis and knowledge extraction of these data in different application domains. Trajectory data are normally available as sample points, and do not carry semantic information, which is of fundamental importance for the comprehension of these data. Therefore, the analysis of trajectory data becomes expensive from a computational point of view and complex from a user's perspective. Enriching trajectories with semantic geographical information may simplify queries, analysis, and mining of moving object data. In this paper we propose a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains. The model is generic enough to represent the important parts of trajectories that are relevant to the application, not being restricted to one specific application. We present an algorithm to compute the important parts and show that the query complexity for the semantic analysis of trajectories will be significantly reduced with the proposed model.