Scientific Data Management
Our approach is to capitalise on the principles of distributed and parallel data management. In particular, we exploit: high-level languages as a basis for data independence and automatic optimisation; data semantics to improve information retrieval and automate data integration; declarative languages (algebra, calculus) to manipulate data and workflows; and highly distributed and parallel environments such as P2P, cluster and cloud. To reflect our approach, we organise our research programme into five complementary themes:
- Data integration, including polystores;
- Query processing, including indexing and privacy; and
- Management of scientific workflows;
- Data analysis, including data mining and statistics;
- Machine learning for high-dimensional data processing and retrieval.