The Community for Technology Leaders
Green Image
Issue No. 05 - September/October (2017 vol. 37)
ISSN: 0272-1716
pp: 28-39
Amilcar Soares Junior , Dalhousie University
Chiara Renso , ISTI-CNR
Stan Matwin , Dalhousie University and Polish Academy of Sciences
The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.
Geographic information systems, Data science, Semantics, Trajectory, Visual analytics, Learning systems

A. Soares Junior, C. Renso and S. Matwin, "ANALYTiC: An Active Learning System for Trajectory Classification," in IEEE Computer Graphics and Applications, vol. 37, no. 5, pp. 28-39, 2017.
275 ms
(Ver 3.3 (11022016))