Permanent link to this recordhttp://hdl.handle.net/10754/271652
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AbstractTrajectory sharing and searching have received significant attention in recent years. In this thesis, we propose and investigate the methods to find and recommend the best trajectory to the traveler, and mainly focus on a novel technique named User Oriented Trajectory Search (UOTS) query processing. In contrast to conventional trajectory search by locations (spatial domain only), we consider both spatial and textual domains in the new UOTS query. Given a trajectory data set, the query input contains a set of intended places given by the traveler and a set of textual attributes describing the traveler’s preference. If a trajectory is connecting/close to the specified query locations, and the textual attributes of the trajectory are similar to the traveler’s preference, it will be recommended to the traveler. This type of queries can enable many popular applications such as trip planning and recommendation. There are two challenges in UOTS query processing, (i) how to constrain the searching range in two domains and (ii) how to schedule multiple query sources effectively. To overcome the challenges and answer the UOTS query efficiently, a novel collaborative searching approach is developed. Conceptually, the UOTS query processing is conducted in the spatial and textual domains alternately. A pair of upper and lower bounds are devised to constrain the searching range in two domains. In the meantime, a heuristic searching strategy based on priority ranking is adopted for scheduling the multiple query sources, which can further reduce the searching range and enhance the query efficiency notably. Furthermore, the devised collaborative searching approach can be extended to situations where the query locations are ordered. Extensive experiments are conducted on both real and synthetic trajectory data in road networks. Our approach is verified to be effective in reducing both CPU time and disk I/O time.