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Making Spatiotemporal Data More Useful: An Entity Linking Approach (2020-2025)

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.

Experts

Professor Shazia Sadiq

Professor
School of Electrical Engineering and Computer Science
Faculty of Engineering, Architecture and Information Technology
Shazia Sadiq
Shazia Sadiq

Associate Professor Jiwon Kim

Associate Professor
School of Civil Engineering
Faculty of Engineering, Architecture and Information Technology
Jiwon Kim
Jiwon Kim