Global GRID for data mining with soft computing on large data bases

The main idea of the proposed project is to prepare selected data mining algorithms, preferably those originating in soft-computing, for running in distributed environments like clusters and global grids. Different techniques will be used, from architectural parallelization of the models, data parallelization of the models and parallel parameter-search methods for sequential models. An analysis will be given focusing on a suitability of the techniques and particular distributed environments in combination with the implemented data mining models. The implementations of methods on distributed computing resources will be tested on a wide variety of databases emerging from industry, medicine and internet in order to investigate their efficiency and robustness.