Making sense of trajectory data: a database approach. This project investigates new challenges related to providing functionality, flexibility and efficiency for large scale trajectory data management and processing. The expected outcome includes significant technical contributions in novel indexing structures and advanced query processing methods for making better use of rich trajectory data.
Algorithmic engineering and complexity analysis of protocols for consensus. Opinions, rankings, observations, votes, gene sequences, sensor-networks in security systems or climate models. Massive datasets and the ability to share information at unprecedented speeds, makes finding the most central representative, the Consensus Problem, extremely complex. This research delivers new insights and new, efficient algorithms.
Making Spatiotemporal Data More Useful: An Entity Linking Approach. 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 contex ....Making Spatiotemporal Data More Useful: An Entity Linking Approach. 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.Read moreRead less
Approximate proximity for applications in data mining and visualization. Data Mining, pattern recognition and visualization of relational information are all important data analysis techniques in which it is essential to determine which data points are in the vicinity of others. The huge size of the data sets involved and the need for real-time interaction preclude the use of conventional methods for the precise computation of the proximity information required. This project will develop efficie ....Approximate proximity for applications in data mining and visualization. Data Mining, pattern recognition and visualization of relational information are all important data analysis techniques in which it is essential to determine which data points are in the vicinity of others. The huge size of the data sets involved and the need for real-time interaction preclude the use of conventional methods for the precise computation of the proximity information required. This project will develop efficient algorithms and data structures for gathering high-quality approximations of the full proximity information, and will use these innovations as the basis for new, practical tools for visualization, and clustering in data mining.Read moreRead less