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Published in:
2017 | OriginalPaper | Chapter
STAVIS 2.0: Mining Spatial Trajectories via Motifs
Authors : Crystal Chen, Arnold P. Boedihardjo, Brian S. Jenkins, Charlotte L. Ellison, Jessica Lin, Pavel Senin, Tim Oates
Published in: Advances in Spatial and Temporal Databases
Publisher: Springer International Publishing
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Abstract
The increase in available spatial trajectory data has led to a massive amount of geo-positioned data that can be exploited to improve understanding of human behavior. However, the noisy nature and massive size of the data make it difficult to extract meaningful trajectory features. In this work, a context-free grammar representation of spatial trajectories is employed to discover frequent segments or motifs within trajectories. Additionally, a set of basis motifs is developed that defines all movement characteristics among a set of trajectories, which can be used to evaluate patterns within a trajectory (intra-trajectory) and between multiple trajectories (inter-trajectory). The approach is realized and demonstrable through the Symbolic Trajectory Analysis and VIsualization System (STAVIS) 2.0, which performs grammar inference on spatial trajectories, mines motifs, and discovers various pattern sets through motif-based analysis.