Name
Abstract | ||
|---|---|---|
The rapid development and commercialization of location acquisition technologies generates large trajectory datasets, that trace moving objects' trips. In this work, we propose a new trajectory mining algorithm, for discovering paths that are frequently followed by the given trajectories, named as corridors. We claim that the moving objects follow common paths-corridors. Detecting corridors from a collection of trajectories is extremely challenging due to the nature of the data (low sampling rates, different speeds, noisy measurements etc.). In this work we propose and evaluate a pipelined algorithm that abstracts from trajectories their underlying frequent paths. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MDM.2018.00032 | 2018 19th IEEE International Conference on Mobile Data Management (MDM) |
Keywords | Field | DocType |
Corridor Learning,Trajectory Mining | Data mining,Computer science,Sampling (statistics),Global Positioning System,Commercialization,Data mining algorithm,Cluster analysis,TRIPS architecture,Trajectory,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-4134-7 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolaos Zygouras | 1 | 25 | 2.28 |
| Dimitrios Gunopulos | 2 | 7171 | 715.85 |