Schema Evolution publication categorizer

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.