Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2021

Abstract

The sequences of user check-ins form semantic trajectories that represent the movement of users through time, along with the types of POIs visited. Extracting patterns in semantic trajectories can be widely used in applications such as route planning and trip recommendation. Existing studies focus on the entire time duration of the data, which may miss some temporally significant patterns. In addition, they require thresholds to define the interestingness of the patterns. Motivated by the above, we study a new problem of finding top-k semantic trajectory patterns w.r.t. a given time period and categories by considering the spatial closeness of POIs. Specifically, we propose a novel algorithm, EC2M that converts the problem from POI-based to cluster-based pattern search and progressively consider pattern sequences with efficient pruning strategies at different steps. Two hashmap structures are proposed to validate the spatial closeness of the trajectories that constitute temporally relevant patterns. Experimental results on real-life trajectory data verify both the efficiency and effectiveness of our method.

Keywords

Pattern search, Trajectory queries, Semantic-temporal

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 26th International Conference on Database Systems for Advanced Applications (DASFAA'21), Virtual Conference, 2021 April 11-14

First Page

439

Last Page

456

City or Country

Virtual Conference

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