Distributing relational model transformation on MapReduce

This paper exploits the high level of abstraction of an existing relational MT language, ATL, and the semantics of a distributed programming model, MapReduce, to build an ATL engine with implicitly distributed execution. The syntax of the language is not modified and no primitive for distribution is added. Efficient distribution of model elements is achieved thanks to a distributed persistence layer, specifically designed for relational MT. We demonstrate the effectiveness of our approach by making an implementation of our solution publicly available and using it to experimentally measure the speed-up of the transformation system while scaling to larger models and clusters.