TY - GEN
T1 - Parallel Subtrajectory Alignment over Massive-Scale Trajectory Data
AU - chen, Lisi
AU - Shang, Shuo
AU - Feng, Shanshan
AU - Kalnis, Panos
N1 - KAUST Repository Item: Exported on 2022-03-29
Acknowledgements: This work was supported by the NSFC (U2001212, 62032001, and 61932004).
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/675994
UR - https://www.ijcai.org/proceedings/2021/497
UR - http://www.scopus.com/inward/record.url?scp=85125434997&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2021/497
DO - 10.24963/ijcai.2021/497
M3 - Conference contribution
SN - 9780999241196
SP - 3613
EP - 3619
BT - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
PB - International Joint Conferences on Artificial Intelligence Organization
ER -