STAVIS 2.0: Mining Spatial Trajectories via Motifs

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.

Authors and Affiliations

1. U.S. Army Engineer Research and Development Center, Alexandria, USA

   Crystal Chen, Arnold P. Boedihardjo, Brian S. Jenkins & Charlotte L. Ellison

2. George Mason University, Fairfax, USA

   Jessica Lin

3. Los Alamos National Lab, Los Alamos, USA

   Pavel Senin

4. University of Maryland Baltimore County, Baltimore, USA

   Tim Oates

Corresponding author

Correspondence to Crystal Chen .

Editors and Affiliations

1. Institute of Computer Science, Heidelberg University, Heidelberg, Germany

   Michael Gertz

2. George Mason University, Fairfax, Virginia, USA

   Matthias Renz

3. University of Queensland, Brisbane, Queensland, Australia

   Xiaofang Zhou

4. ESRI, University of Minnesota, Minneapolis, Minnesota, USA

   Erik Hoel

5. Auburn University, Auburn, Alabama, USA

   Wei-Shinn Ku

6. Free University of Berlin, Dahlem, Berlin, Germany

   Agnes Voisard

7. Microsoft, Redmond, Washington, USA

   Chengyang Zhang

8. California State University, Sacramento, California, USA

   Haiquan Chen

9. LinkedIn, Sunnyvale, California, USA

   Liang Tang

10. University of North Texas, Denton, Texas, USA

    Yan Huang

11. Virginia Tech, Falls Church, Virginia, USA

    Chang-Tien Lu

12. Oracle, Redwood Shores, California, USA

    Siva Ravada

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