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Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories

Summary: Splitter mines fine-grained sequential patterns in semantic trajectories via coarse patterns and top-down subpatterns. Uses weighted snippet shift clustering and splitting to scale, enforcing spatial compactness, semantic coherence, and temporal continuity. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11141
Venue
VLDB
Year
2014
Pagerank
5.4119882e-05
Overall Rank
8,538 | 41.43%
DOI
10.14778/2732939.2732948

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhang_vldb14,
        title = {{Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories}},
        author = {Zhang, Chao and Han, Jiawei and Shou, Lidan and Lu, Jiajun and La Porta, Thomas},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {9},
        pages = {769--780},
        doi = {10.14778/2732939.2732948},
        url = {https://doi.org/10.14778/2732939.2732948},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
12,018 Efficient Mining of Regional Movement Patterns in Semantic Trajectories 2017 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,381 Finding Time Period-Based Most Frequent Path in Big Trajectory Data 2013 SIGMOD 0.00010965263
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
Previous Page 1 / 1 Next

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