STKST-I: An Efficient Semantic Trajectory Search by Temporal and Semantic Keywords

The popularity of intelligent mobile devices has increased because they are not only convenient for people but also produce a large number of GPS trajectories. Semantic trajectories can be obtained by adding semantic information such as landmarks and activities to raw trajectories. Keyword queries in semantic trajectory databases that return the relevant places/routes have attracted increasing attention from researchers in recent years. However, existing works only consider the spatial and textual features of keywords, which cannot answer queries with temporal requirements. Simply modifying existing algorithms to support temporal requirements may lead to errors and low efficiency. Additionally, they match keywords only by string similarity without considering their semantic meanings. In this paper, we study the problem of efficient spatiotemporal keyword search in semantic trajectories (STKST). Given the position of a user and a set of keywords with temporal constraints, we aim to efficiently retrieve top -k trajectories that contain the most semantically and temporally relevant keywords and are close to the position of the user. To measure the goodness of a trajectory regarding the query, we devise a new integrated similarity measure by considering information from three aspects (spatial, temporal, and semantic). Then we develop a novel hybrid spatial-temporal-semantic index (STS-I) to organize these three kinds of information in trajectories in the form of tree structure. Finally, we propose a new algorithm STKST-I to efficiently prune unqualified trajectories based on the lower and upper bounds derived from the STS-I index. Extensive experimental studies are conducted on real trajectory datasets to verify the performance of our methods.