Applying Generic Model Management to Data Mapping

Mapping between heterogeneous data is a central problem in many dataintensive applications. In particular, using one mapping language causes serious limitations and makes mapping management difficult. In this paper, we propose a solution that can better control the trade-off between genericity, expressiveness and efficiency of mappings. Our solution considers mappings as models and exploits specific mapping engines. We define model weaving as a generic way to establish element correspondences. Weaving models may then be used by a model transformation language to translate source model(s) into target model(s). To validate our solution, we implemented a mapping prototype, called AMW, and used it for experimenting with significant application scenarios.