AutoGen: Easing model management through two levels of abstraction

Song, G; Kong, J; Zhang, K
Song, G
Kong, J
Zhang, K
Journal of Visual Languages & Computing
Citations range: 
1 - 9

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.