Name
Abstract | ||
|---|---|---|
In VAST Challenge 2017, we propose an interactive and collaborative visual analytic system for the analysis of traffic sensor data. Our system fully incorporates the power of spatial-temporal visualization, sequence mining techniques and collaborative analysis. It allows users to conduct multi-facet and interactive data analysis in a highly efficient way. We discuss technical details in this report, and demonstrate the effectiveness of our system via convincing cases. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/VAST.2017.8585432 | 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) |
Keywords | Field | DocType |
interactive data analysis,traffic sensor data,collaborative visual analytic system,spatial-temporal visualization,sequence mining techniques | Data mining,Logic gate,Computer science,Visualization,Visual analytics,Sequential Pattern Mining,Trajectory | Conference |
ISSN | ISBN | Citations |
2325-9442 | 978-1-5386-3164-5 | 0 |
PageRank | References | Authors |
0.34 | 1 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chufan Lai | 1 | 27 | 3.69 |
| Qiangqiang Liu | 2 | 0 | 0.68 |
| Lu Feng | 3 | 2 | 2.40 |
| Chenglei Yue | 4 | 0 | 0.34 |
| Xi Chen | 5 | 226 | 49.58 |
| Yang Hu | 6 | 1 | 1.11 |
| Zhanyi Wang | 7 | 0 | 0.34 |
| Pengju Teng | 8 | 0 | 0.34 |
| Xiaoru Yuan | 9 | 1157 | 70.28 |