WSRF services for learning classifiers from Data Grid

In this paper, we present the Weka4GML architecture, a new framework based on WSRF technology for developing meta-learning methods to deal with datasets distributed among data grid. This framework extends the Weka toolkit to support distributed execution of data mining methods, like meta-learning. The architecture and the behavior of the proposed framework are described in this paper. We also detail the different steps needed to execute a meta-learning process on a Globus environment.