TY - GEN
T1 - STORM
T2 - 34th ACM International Conference on Information and Knowledge Management, CIKM 2025
AU - Han, Xiaolin
AU - Zhou, Yonghao
AU - Ma, Chenhao
AU - Li, Fang
AU - Shang, Xuequn
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/11/10
Y1 - 2025/11/10
N2 - Similar trajectory retrieval is crucial for maritime trajectory data analysis. However, due to issues such as errors in maritime positioning devices and the accuracy limitations of satellite positioning systems at sea, maritime trajectory data often exhibit characteristics of non-uniform sampling. Existing algorithms struggle to effectively model the irregularity of non-uniformly sampled maritime trajectories, leading to reduced performance in similar trajectory retrieval. In this demonstration, we present STORM, a system designed to effectively retrieve the top-k similar trajectories, which supports both user-specified and automated query settings. STORM utilizes a learnable Fourier-based encoding method to efficiently extract spatiotemporal features from non-uniform trajectories, significantly enhancing the model's performance in similar trajectory retrieval. Our demonstration shows that, compared to state-of-the-art (SOTA) methods, STORM achieves a 41.9% improvement in performance for similar trajectory retrieval on non-uniform maritime data. Our demonstration video is available at https://github.com/itszzzyyy/STORM.
AB - Similar trajectory retrieval is crucial for maritime trajectory data analysis. However, due to issues such as errors in maritime positioning devices and the accuracy limitations of satellite positioning systems at sea, maritime trajectory data often exhibit characteristics of non-uniform sampling. Existing algorithms struggle to effectively model the irregularity of non-uniformly sampled maritime trajectories, leading to reduced performance in similar trajectory retrieval. In this demonstration, we present STORM, a system designed to effectively retrieve the top-k similar trajectories, which supports both user-specified and automated query settings. STORM utilizes a learnable Fourier-based encoding method to efficiently extract spatiotemporal features from non-uniform trajectories, significantly enhancing the model's performance in similar trajectory retrieval. Our demonstration shows that, compared to state-of-the-art (SOTA) methods, STORM achieves a 41.9% improvement in performance for similar trajectory retrieval on non-uniform maritime data. Our demonstration video is available at https://github.com/itszzzyyy/STORM.
KW - similar trajectory retrieval
KW - spatio-temporal trajectories
UR - https://www.scopus.com/pages/publications/105023198002
U2 - 10.1145/3746252.3761489
DO - 10.1145/3746252.3761489
M3 - 会议稿件
AN - SCOPUS:105023198002
T3 - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
SP - 6644
EP - 6648
BT - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery, Inc
Y2 - 10 November 2025 through 14 November 2025
ER -