Please note: This PhD seminar will be given online.
Ivens Portugal, PhD candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Paulo Alencar, Donald Cowan, Daniel Berry
Spatial-temporal data analysis aims at uncovering useful insights and patterns from data that is associated with a location and that changes with time. Spatial-temporal data can comprise massive datasets obtained from multiple sources, including mobile devices, cameras, radar, and other types of sensors. Traditional analysis techniques allow researchers to perform several tasks, including classification, regression, and clustering. Specifically, clustering methods have been widely adopted in domains such as transportation, smart cities, and astronomy. However, current clustering techniques fail to analyze a moving cluster from its start to finish, limiting themselves to investigating static clusters. This study introduces a framework that takes into consideration the entire life of a moving cluster and describes its lifetime based on dynamic spatial-temporal relationships that the cluster has with other clusters or trajectories.
To join this PhD seminar on Zoom, please go to https://uwaterloo.zoom.us/j/94124153604?pwd=MnQ3L3V4d29PdnVyOWJ0SkhpQmxrZz09.
200 University Avenue West
Waterloo, ON N2L 3G1