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A General and Parallel Platform for Mining Co-Movement Patterns over Large-scale Trajectories

Summary: Unifies prior co-movement definitions into a general pattern class for large-scale trajectories. Presents two parallel Spark-based frameworks for scalable mining, and demonstrates effectiveness on real datasets with hundreds of millions of points. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11717
Venue
VLDB
Year
2017
Pagerank
7.2293286e-05
Overall Rank
3,645 | 75.00%
DOI
10.14778/3025111.3025114

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{fan_vldb17,
        title = {{A General and Parallel Platform for Mining Co-Movement Patterns over Large-scale Trajectories}},
        author = {Fan, Qi and Zhang, Dongxiang and Wu, Huayu and Tan, Kian-Lee},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {4},
        pages = {313},
        doi = {10.14778/3025111.3025114},
        url = {https://doi.org/10.14778/3025111.3025114},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
27 Fast Algorithms for Mining Association Rules 1994 VLDB 0.00052255472
1,319 SkewTune: Mitigating Skew in MapReduce Applications 2012 SIGMOD 0.00011175005
1,755 Swarm: Mining Relaxed Temporal Moving Object Clusters 2010 VLDB 9.8211863e-05
1,885 Discovery of Convoys in Trajectory Databases 2008 VLDB 9.5362394e-05
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