基于自然语言的不确定人体轨迹可视化查询方法
A Natural-language-based Visual Query Approach of Uncertain Human Trajectories
摘要：
可视化查询对于交互式地探索海量轨迹数据至关重要。然而，数据的不确定性给满足高级分析要求带来了深刻的挑战。一方面，许多基础数据不包含准确的地理坐标，例如，移动电话的位置仅涉及区域（即，移动小区站），而不是精确的GPS坐标。另一方面，领域专家和普通用户更喜欢使用自然的方式，例如使用自然语言句子来访问和分析大量运动数据。在本文中，我们提出了一种可视化分析方法，可以从文本句子中提取时空约束，并支持对不确定移动轨迹数据的有效查询方法。该方法利用兴趣点及其所覆盖区域的语义信息对海量、空间不确定的轨迹进行编码，并采用有效的索引机制将轨迹文档存储在文本数据库中。可视化界面有助于查询条件规范，情境感知可视化和大型轨迹数据的语义探索。真实世界人类移动数据集上的使用场景证明了我们方法的有效性。
英文摘要：
Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POIs and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
