论文标题
一项有关轨迹数据管理，分析和学习的调查
A Survey on Trajectory Data Management, Analytics, and Learning
论文作者
论文摘要
传感器和移动设备的最新进展使城市轨迹数据的可用性和收集史无前例地增加了，从而增加了对管理和分析所产生数据的更有效方法的需求。在这项调查中，我们全面回顾了轨迹数据管理的最新研究趋势，范围从轨迹预处理，存储，常见的轨迹分析工具，例如查询仅空间和空间文本轨迹数据以及轨迹聚类等。我们还探索了四个与交互式或实时处理中轨迹数据相关的密切相关的分析任务。还首次审查了深度轨迹学习。最后，我们概述了轨迹数据管理系统应具有的基本素质，以最大程度地提高灵活性。
Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being produced. In this survey, we comprehensively review recent research trends in trajectory data management, ranging from trajectory pre-processing, storage, common trajectory analytic tools, such as querying spatial-only and spatial-textual trajectory data, and trajectory clustering. We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing. Deep trajectory learning is also reviewed for the first time. Finally, we outline the essential qualities that a trajectory data management system should possess in order to maximize flexibility.
