Making Spatiotemporal Data More Useful: An Entity Linking Approach (2020–2022)

Abstract:
This project aims to establish a methodology for spatiotemporal entity linking by utilising object movement traces to support database integration and data quality management for the next-generation of data where spatiotemporal attributes are ubiquitous. It expects to develop a novel entity linking paradigm for automatic, efficient and reliable spatiotemporal data integration together with a new data privacy study in this context. Expected outcome include new database technologies for data signature generation and similarity-based search, and improved location data privacy protection methods. This project should provide significant benefits to all areas where high quality spatiotemporal data fusion is essential to meaningful data analysis.
Grant type:
ARC Discovery Projects
Researchers:
  • ARC DECRA Senior Research Fellow
    School of Information Technology and Electrical Engineering
    Faculty of Engineering, Architecture and Information Technology
  • ARC DECRA
    School of Civil Engineering
    Faculty of Engineering, Architecture and Information Technology
Funded by:
Australian Research Council