AutoGen: Easing model management through two levels of abstraction ?
Due to its extensive potential applications, model management has attracted many research interests and gained great progress. To provide easy-to-use interfaces, we have proposed a graph transformation-based model management approach that provides intuitive interfaces for manipulation of graphical data models. The approach consists of two levels of graphical operators: low-level customizable operators and high-level generic operators, both of which consist of a set of graph transformation rules. Users need to program or tune the low-level operators for desirable results. To further improve the ease-of-use of the graphical model management, automatic generation of low level of operators is highly desirable. The paper formalizes specifications of low- and high-level operators and proposes a generator to automatically transform high-level operators into low-level operators upon specific input data models. Based on graph transformation theoretical foundation, we design an algorithm for the generator to automatically produce low-level operators from input data models and mappings according to a high-level operator. The generator, called AutoGen, therefore eliminates many tedious specifications and thus eases the use of the graphical model management system.
model management graph transformation graph grammar visual programming schema interoperation