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• W3009448889 abstract "Similar trajectory search is a fundamental problem and has been well studied over the past two decades. However, the similar subtrajectory search (SimSub) problem, aiming to return a portion of a trajectory (i.e., a subtrajectory) which is the most similar to a query trajectory, has been mostly disregarded despite that it could capture trajectory similarity in a finer-grained way and many applications take subtrajectories as basic units for analysis. In this paper, we study the SimSub problem and develop a suite of algorithms including both exact and approximate ones. Among those approximate algorithms, two that are based on deep reinforcement learning stand out and outperform those non-learning based algorithms in terms of effectiveness and efficiency. We conduct experiments on real-world trajectory datasets, which verify the effectiveness and efficiency of the proposed algorithms." @default.
• W3009448889 created "2020-03-13" @default.
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• W3009448889 date "2020-03-05" @default.
• W3009448889 modified "2023-09-23" @default.
• W3009448889 title "Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning" @default.
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• W3009448889 doi "https://doi.org/10.48550/arxiv.2003.02542" @default.
• W3009448889 hasPublicationYear "2020" @default.
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