A Natural-language-based Visual Query Approach of Uncertain Human Trajectories
This work addresses the challenge of interactively exploring uncertain trajectory data for domain experts and general users, offering a natural language-based query method, but it is incremental as it builds on existing visual analytics and indexing techniques.
The paper tackles the problem of querying uncertain human trajectory data, such as mobile phone locations with region-level accuracy, by developing a visual analytics approach that extracts spatial-temporal constraints from natural language sentences, enabling effective querying and exploration of massive movement data.
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.