TrajViz: A Tool for Visualizing Patterns and Anomalies in Trajectory
Abstract
Visualizing frequently occurring patterns and potentially unusual behaviors in trajectory can provide valuable insights into activities behind the data. In this paper, we introduce TrajViz, a motif (frequently repeated subsequences) based visualization software that detects patterns and anomalies by inducing “grammars” from discretized spatial trajectories. We consider patterns as a set of sub-trajectories with unknown lengths that are spatially similar to each other. We demonstrate that TrajViz has the capacity to help users visualize anomalies and patterns effectively.
Cite
Text
Gao et al. "TrajViz: A Tool for Visualizing Patterns and Anomalies in Trajectory." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_45Markdown
[Gao et al. "TrajViz: A Tool for Visualizing Patterns and Anomalies in Trajectory." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/gao2017ecmlpkdd-trajviz/) doi:10.1007/978-3-319-71273-4_45BibTeX
@inproceedings{gao2017ecmlpkdd-trajviz,
title = {{TrajViz: A Tool for Visualizing Patterns and Anomalies in Trajectory}},
author = {Gao, Yifeng and Li, Qingzhe and Li, Xiaosheng and Lin, Jessica and Rangwala, Huzefa},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {428-431},
doi = {10.1007/978-3-319-71273-4_45},
url = {https://mlanthology.org/ecmlpkdd/2017/gao2017ecmlpkdd-trajviz/}
}