GridML: Next generation data mining on high performance, parallel distributed systems

The project aims at developing a datamining software prototype for high performance, distributed and parallel systems (grids). The software should free the human user of the algorithm selection and scheduling decisions, and by automating these permits the generation of higher quality datamining models. The innovative element of the project is the optimisation of the scheduling of datamining algorithms enabled by meta-level learning. The prototype will support the documentation and verification of datamining projects, while it remains expandable thanks to its architecture. Special attention is paid to data privacy issues. After the termination of the project, the prototype and its subsequent versions will be available for non-profit research purposes. Following up on the results of the project, the members will consider the commercial deployment of a datamining grid-based product.