Semantic-aware Query Processing for Activity Trajectories

Nowadays, users of social networks like tweets and weibo have generated massive geo-tagged records, and these records reveal their activities in the physical world together with spatio-temporal dynamics. Existing trajectory data management studies mainly focus on analyzing the spatio-temporal properties of trajectories, while leaving the understanding of their activities largely untouched. In this paper, we incorporate the semantic analysis of the activity information embedded in trajectories into query modelling and processing, with the aim of providing end users more accurate and meaningful trip recommendations. To this end, we propose a novel trajectory query that not only considers the spatio-temporal closeness but also, more importantly, leverages probabilistic topic modelling to capture the semantic relevance of the activities between data and query. To support efficient query processing, we design a novel hybrid index structure, namely ST-tree, to organize the trajectory points hierarchically, which enables us to prune the search space in spatial and topic dimensions simultaneously. The experimental results on real datasets demonstrate the efficiency and scalability of the proposed index structure and search algorithms.