Trajectory-Based Spatiotemporal Entity Linking

TL;DR

This paper introduces a trajectory-based spatiotemporal entity linking method that uses concise signatures and a novel indexing structure to improve accuracy and efficiency in matching moving objects across datasets.

Contribution

It proposes new signature representations, a dimension reduction strategy, and the WR-tree index, advancing the state-of-the-art in spatiotemporal entity linking.

Findings

Outperforms existing methods in accuracy.

Achieves faster search times.

Demonstrates robustness on real-world datasets.

Abstract

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a k-nearest neighbor (k-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency.