SDSTM: a Self-Describing Semantic Trajectory Model for ubiquitous situations
Xingang Wang, Zhigang Gai, Yi Zheng, Shanshan Xu, Liu Yong, Jinhua Li · 2018
In the literature, semantic annotation on spatial trajectories is not stretched out or refined well in a structured way, which makes it cannot represent rich context or activity semantics. In this paper, we present a Self-Describing Semantic Trajectories Model (SDSTM) with meta parts called Scenario and Scenario-Instance, and these parts are enforced in JSON, and can describe entities and their relationships in ubiquitous situations in a structured and self-describing way. Through our model, spatial data and their context and activity semantics can be fused into a whole structured data for easy analysis further.