Title :
WSRF services for learning classifiers from Data Grid
Author :
Ben Haj Hmida, Moez ; Slimani, Yahya
Author_Institution :
Dept. of Comput. Sci., Fac. of Sci. of Tunisia, Tunis
Abstract :
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.
Keywords :
Web services; data mining; grid computing; learning (artificial intelligence); WSRF services; Web service resource framework; Weka toolkit; Weka4GML architecture; data grid; data mining methods; learning classifiers; meta-learning methods; Computer architecture; Data mining; Databases; Distributed computing; Grid computing; Large-scale systems; Learning systems; Middleware; Partitioning algorithms; Web services;
Conference_Titel :
Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference on
Conference_Location :
Rabat
Print_ISBN :
978-1-4244-3807-5
Electronic_ISBN :
978-1-4244-3806-8
DOI :
10.1109/AICCSA.2009.5069293
