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Quantifying Point Contributions: A Lightweight Framework for Efficient and Effective Query-Driven Trajectory Simplification

Summary: MLSimp: a mutual-learning, query-driven trajectory simplification framework combining GNN-TS (attention-based GNN capturing globality and uniqueness to avoid iterative pruning) and Diff-TS (diffusion-based signal amplification for low-rate retention). Cuts simplification time 42–70% and improves query accuracy up to 34.6% vs eight baselines. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14064
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,750 | 25.22%
DOI
10.14778/3705829.3705858

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10,272 MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data 2026 VLDB 4.1945683e-05
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