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)
Incoming Non-self Citations Over Time
Authors
- 1. Chao Zhang
- 2. Jiawei Han
- 3. Lidan Shou
- 4. Jiajun Lu
- 5. Thomas La Porta
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,815 | Efficient Mining of Regional Movement Patterns in Semantic Trajectories | 2017 | VLDB | 4.1945683e-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 |
|---|---|---|---|---|
| 1,115 | Finding Time Period-Based Most Frequent Path in Big Trajectory Data | 2013 | SIGMOD | 0.00013894562 |
| 1,718 | Swarm: Mining Relaxed Temporal Moving Object Clusters | 2010 | VLDB | 0.00010789285 |
| 1,745 | Discovery of Convoys in Trajectory Databases | 2008 | VLDB | 0.00010702338 |
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