Answering Top-k Exemplar Trajectory Queries

We study a new type of spatial-textual trajectory search: the Exemplar Trajectory Query (ETQ), which specifies one or more places to visit, and descriptions of activities at each place. Our goal is to efficiently find the top-k trajectories by computing spatial and textual similarity at each point. The computational cost for pointwise matching is significantly higher than previous approaches. Therefore, we introduce an incremental pruning baseline and explore how to adaptively tune our approach, introducing a gap-based optimization and a novel two-level threshold algorithm to improve efficiency. Our proposed methods support order-sensitive ETQ with a minor extension. Experiments on two datasets verify the efficiency and scalability of our proposed solution.