Name
Abstract | ||
|---|---|---|
Mining large-scale trajectory data streams (of moving objects) has been of ever increasing research interest due to an abundance of modern tracking devices and its large number of critical applications. A challenging task in this domain is that of mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. To this end, we introduce Trajectolizer, an online system for interactive analysis and exploration of trajectory group dynamics over time and space. We describe the system and demonstrate its effectiveness on discovering group patterns on trajectories of pedestrians. The system architecture and methods are general and can be used to perform group analysis of any domain-specific trajectories. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MDM.2018.00053 | 2018 19th IEEE International Conference on Mobile Data Management (MDM) |
Keywords | Field | DocType |
Trajectory mining,group pattern mining | Data mining,Data stream mining,Data visualization,Task analysis,Computer science,Spacetime,Vehicle dynamics,Systems architecture,Group analysis,Trajectory,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-4134-7 | 1 | 0.36 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdullah Sawas | 1 | 2 | 0.77 |
| Abdullah Abuolaim | 2 | 6 | 3.18 |
| Mahmoud Afifi | 3 | 35 | 10.85 |
| Manos Papagelis | 4 | 357 | 24.03 |