论文标题
基于轨迹的时空实体链接
Trajectory-Based Spatiotemporal Entity Linking
论文作者
论文摘要
基于轨迹的时空实体链接是根据其运动轨迹匹配不同数据集中的相同移动对象。这是支持时空数据集成和分析的基本步骤。在本文中，我们研究了使用从其轨迹中提取的有效和简洁的标志联系时空实体的问题。这个链接问题被形式化为签名上的K-Neartible邻居（K-NN）查询。研究了四种表示策略（顺序，时间，空间和时空）和两个定量标准（通用性和独立性）以进行签名结构。一种简单而有效的降低策略与一种称为WR-Tree的新型索引结构一起制定了，以加快搜索的速度。提出了许多优化方法来提高链接的准确性和鲁棒性。我们在现实世界数据集上进行的广泛实验验证了我们的方法优于最先进的解决方案，从精度和效率方面。
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.
