Advances in Distributed and Parallel Knowledge Discovery
Advances in Distributed and Parallel Knowledge Discovery
Communications of the ACM
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Web Services Composition for Distributed Data Mining
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
Programming scientific and distributed workflow with Triana services: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Distributed data mining services leveraging WSRF
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Data Mining in Grid Computing Environments
Data Mining in Grid Computing Environments
Weka4WS: a WSRF-enabled weka toolkit for distributed data mining on grids
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Adapting the weka data mining toolkit to a grid based environment
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
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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.