Authors: Moez Ben Haj Hmida, Yahya Slimani
Addresses: Department of Computer Science, Faculty of Sciences of Tunis, Campus Universitaire, 2092 El Manar, Tunis, Tunisia. ' Department of Computer Science, Faculty of Sciences of Tunis, Campus Universitaire, 2092 El Manar, Tunis, Tunisia
Abstract: The Weka4GML framework has been designed to meet the requirements of distributed data mining. In this paper, we present the Weka4GML architecture 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 behaviour 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. Finally, the framework has been discussed and compared to related works.
Keywords: distributed data mining; meta-learning; grid computing; distributed datasets; web service resource framework; WSRF.
International Journal of Communication Networks and Distributed Systems, 2010 Vol.5 No.3, pp.214 - 228
Published online: 31 Aug 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article