Database Paper Browser

Back to papers

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
10953
Venue
VLDB
Year
2014
Pagerank
4.5435639e-05
Overall Rank
8,302 | 42.25%
DOI
-

Incoming Non-self Citations Over Time

Authors

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
Previous Page 1 / 1 Next

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
Previous Page 1 / 1 Next

Semantically Similar Papers