Organisation(s):

Dar es Salaam Institute of Technology – Tanzania
TERNET – Tanzania
Brunel University London – United Kingdom
Department of Physics and Astronomy – University of Catania – Italy

Champion(s):

Mr. Stephan N. MGAYA

Other Member(s):

Dr. Joseph W. Matiko
Dr. Simon TAYLOR
Dr. Anastasia ANAGNOSTOU
Eng. Mario TORRISI
Prof. Roberto BARBERA

Use Case Description

Integration of WEKA in a Science Gateway
Integration of the Waikato Environment for Knowledge Analysis (WEKA) collection of machine learning algorithms for data mining tasks in a Science Gateway. WEKA will be trained with the Wisconsin Breast Cancer datasets from the UCI Machine Learning Repository to help doctors in Tanzania to distinguish breast cancer from benign samples.


e-Infrastructure Services Exploited

The main outcome of the use case is a JSR 168/286 standard-based “portlet”, which will be integrated in the Africa Grid Science Gateway.

For running WEKA, the Africa Grid Science Gateway is connected to a FutureGateway API Server – developed in the context of the INDIGO-DataCloud project – which seamlessly executes jobs on local, Grid and Cloud resources belonging to the Africa & Arabia Regional Operation Centre.

Current Implementation of the Use Case

Breast cancer analysis with WEKA, Code repository

References

Presentation 1Presentation 2Poster