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Trajectory Similarity Measurement: An Efficiency Perspective

Summary: Reframes trajectory similarity around efficiency: compares time complexity and empirical runtime of classic non-learned measures (e.g., Hausdorff) against embedding-based deep models. Shows non-learned often match or beat learned approaches for one-off queries; embeddings only win with precomputation, with self-attention models giving the best speed–accuracy tradeoff. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13459
Venue
VLDB
Year
2024
Pagerank
5.0321577e-05
Overall Rank
6,512 | 54.70%
DOI
10.14778/3665884.3665858

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