Parallel Subtrajectory Alignment over Massive-Scale Trajectory Data

Lisi chen, Shuo Shang, Shanshan Feng, Panos Kalnis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


We study the problem of subtrajectory alignment over massive-scale trajectory data. Given a collection of trajectories, a subtrajectory alignment query returns new targeted trajectories by splitting and aligning existing trajectories. The resulting functionality targets a range of applications, including trajectory data analysis, route planning and recommendation, ridesharing, and general location-based services. To enable efficient and effective subtrajectory alignment computation, we propose a novel search algorithm and filtering techniques that enable the use of the parallel processing capabilities of modern processors. Experiments with large trajectory datasets are conducted for evaluating the performance of our proposal. The results show that our solution to the subtrajectory alignment problem can generate high-quality results and are capable of achieving high efficiency and scalability.
Original languageEnglish (US)
Title of host publicationProceedings of the Thirtieth International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence Organization
Number of pages7
ISBN (Print)9780999241196
StatePublished - Aug 2021

Bibliographical note

KAUST Repository Item: Exported on 2022-03-29
Acknowledgements: This work was supported by the NSFC (U2001212, 62032001, and 61932004).


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