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

Summary: MLSimp jointly uses attention-based GNNs to score trajectory-point globality and local uniqueness without iterative deletion, and diffusion models to preserve salient points under aggressive compression. It cuts simplification time 42–70% and improves query accuracy up to 34.6%. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14252
Venue
VLDB
Year
2025
Pagerank
5.3766157e-05
Overall Rank
8,727 | 40.13%
DOI
10.14778/3705829.3705858

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{song_vldb25,
        title = {{Quantifying Point Contributions: A Lightweight Framework for Efficient and Effective Query-Driven Trajectory Simplification}},
        author = {Song, Yumeng and Gu, Yu and Li, Tianyi and Li, Yushuai and Jensen, Christian S. and Yu, Ge},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {2},
        pages = {453--465},
        doi = {10.14778/3705829.3705858},
        url = {https://doi.org/10.14778/3705829.3705858},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,222 DimWeaver: Dimensional Weaved Trajectory Compression 2026 SIGMOD 5.093636e-05
10,561 MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data 2026 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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