A Contextual Edit Distance for Semantic Trajectories

The understanding of daily human activity is an active research topic. Thanks to GES and smartphones, human movements can be monitored and analyzed. In addition, by exploiting Linked Open Data and user personal data, semantic labels and annotations can be added to movements. Thus, semantic trajectories call be considered as sequences of timestamped activities where each activity is described by a semantic label. In this context, a major challenge is the comparison of such semantic trajectories, looking to extract and learning similar human mobility behaviors. We propose CED (Contextual Edit Distance), a generic similarity measure for semantic sequences comparison which improve the Edit Distance to take into account the context similarity between elements in the sequence. CED is configurable to any sequence data and business needs.