Name
Abstract | ||
|---|---|---|
We present a novel and configurable synthetic data generator for evolving region trajectories that emulates certain characteristics of a given input dataset, such as the spatial position, velocity, lifespan, and geometry shape and size. This tool aims to facilitate faster prototyping and evaluation of new spatiotemporal data mining algorithms that operate on a specific type of trajectory data, of which there may be only a limited quantity readily available. We demonstrate through experiments and visualizations that the salient characteristics of a generated dataset are sufficiently similar to those of the source dataset. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDMW.2017.42 | 2017 IEEE International Conference on Data Mining Workshops (ICDMW) |
Keywords | Field | DocType |
Spatiotemporal trajectories,evolving region trajectories,synthetic dataset generator | Data mining,Histogram,Computer science,Visualization,Synthetic data,Data mining algorithm,Trajectory,Salient | Conference |
ISSN | ISBN | Citations |
2375-9232 | 978-1-5386-3801-9 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sajitha Naduvil-Vadukootu | 1 | 0 | 0.68 |
| Berkay Aydin | 2 | 40 | 10.75 |
| Michael A. Schuh | 3 | 71 | 8.03 |
| Rafal A. Angryk | 4 | 271 | 45.56 |