Transformation Learning in the Context of Model-Driven Data Warehouse: An Experimental Design Based on Inductive Logic Programming

Model transformation in the context of Model-Driven Data Warehouse is ensured by human experts. It generates an exorbitant cost and requires high proficiency. We propose in this paper a machine learning approach to reduce the expert contribution in the transformation process. We propose to express the model transformation problem as an Inductive Logic Programming one and to use existing project traces to find the best business transformation rules. We used the Aleph ILP system to learn such rules. Obtained results show that found rules are close to expert ones. Within our application context, we need to deal with several dependent concepts. Taking into account work in Layered Learning, we propose a new methodology that automatically updates the background knowledge of the concepts to be learned. Experimental results support the conclusion that this approach is suitable to solve this kind of problem.