Semantic Trajectory Modelling in Indoor and Outdoor Spaces

Modern and connected mobilities have generated an explosive growth of location-based information. Such location data together with related crowd-sensed information, are noticeably available for humans navigating in both indoor and outdoor spaces. Considering the diversity of such multi-environment spaces, and where mobility occurs, raises several data modelling, management and processing research challenges. This not only implies to develop appropriate database architecture for large streamed data but also to identify the most appropriate data abstractions to model these human trajectories at the semantic level. While recent approaches often considered this issue using common stops and moves model, this does not completely cover the multi-dimensional contextual information that arises for humans navigating through indoor and outdoor spaces. This paper introduces a model of semantic trajectories evolving in both indoor and outdoor spaces, and cross-related with contextual information. This model defines a semantic trajectory considering multiple collaborative data semantics at different abstraction levels, and where trajectory segmentation relies on evolving semantic values. This enables (i) a unified indoor and outdoor spatial representation for trajectory annotation and (ii) multidimensional data integration and management. The final aim is to support semantic querying for a better understanding of human mobilities in urban environments.