User trajectory data is becoming increasingly accessible due to the prevalence of GPS-equipped devices such as smartphones. Many existing studies focus on querying trajectories that are similar to each other in their entirety. We observe that trajectories partially similar to each other contain useful information about users' travel patterns which should not be ignored. Such partially similar trajectories are critical in applications such as epidemic contact tracing. We thus propose to query trajectories that are within a given distance range from each other for a given period of time. We formulate this problem as a sub-trajectory similarity join query named as the STS-Join. We further propose a distributed index structure and a query algorithm for STS-Join, where users retain their raw location data and only send obfuscated trajectories to a server for query processing. This helps preserve user location privacy which is vital when dealing with such data. Theoretical analysis and experiments on real data confirm the effectiveness and the efficiency of our proposed index structure and query algorithm.

翻译：由于智能手机等GPS装置的普及性,用户轨迹数据越来越容易获得。许多现有研究侧重于查询彼此完全相似的轨迹。我们观察到,部分相似的轨迹包含关于用户旅行模式的有用信息,不应忽视。这种部分相似的轨迹在流行病接触追踪等应用中至关重要。因此,我们提议在一定的一段时间内查询在一定距离内彼此之间的轨迹。我们把这个问题作为子轨迹相似性结合名为STS-Join的查询。我们进一步为STS-Join提出了一个分布式索引结构和查询算法,用户保留其原始位置数据,只将模糊的轨迹传送到服务器进行查询处理。这有助于维护在处理这些数据时至关重要的用户位置隐私。关于真实数据的理论分析和实验证实了我们拟议的索引结构和查询算法的有效性和效率。