OR

Browse By

Detection of Location Significance from Quality Enhanced Trajectory Data (2012–2014)

Abstract:
The proliferation of GPS-enabled mobile devices has contributed to accumulation of large-scale data on trajectories of moving objects, and presented an unprecedented opportunity to discover & share new knowledge, such as location significance. Existing technologies lack the ability to provide meaningful rankings on locations respective to user communities due to lack of consideration for some fundamental characteristics of trajectory data such as uncertainty, lack of semantics and lack of context. This project aims at overcoming the above by building solutions on quality enhanced trajectory data & achieving ranking of physical locations in the geographical space similar to what was achieved by page ranking in cyberspace.
Grant type:
ARC Discovery Projects
Researchers:

  • Professor Shazia Sadiq

    Professor
    School of Electrical Engineering and Computer Science
    Faculty of Engineering, Architecture and Information Technology
Funded by:
Australian Research Council