MoCo-AIS is a MoCo-based contrastive learning framework that learns vessel trajectory embeddings and improves similarity computation over baselines on large-scale real-world AIS datasets while offering a benchmarking platform.
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TSseek approximates time series as line segments and regex queries as bounding rectangles, then uses a distributed spatial index (TSseek-X) to support efficient exact whole-matching and subsequence-matching queries.
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MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories
MoCo-AIS is a MoCo-based contrastive learning framework that learns vessel trajectory embeddings and improves similarity computation over baselines on large-scale real-world AIS datasets while offering a benchmarking platform.
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TSseek: Regular Expression-Based Similarity Search for Distributed Time Series Datasets
TSseek approximates time series as line segments and regex queries as bounding rectangles, then uses a distributed spatial index (TSseek-X) to support efficient exact whole-matching and subsequence-matching queries.