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GeRoMe: A Generic Role Based Metamodel for Model Management

  • David Kensche
  • Christoph Quix
  • Mohamed Amine Chatti
  • Matthias Jarke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4380)

Abstract

The goal of Model Management is the development of new technologies and mechanisms to support the integration, evolution and matching of data models at the conceptual and logical design level. Such tasks are to be performed by means of a set of model management operators which work on models and their elements, without being restricted to a particular metamodel (e.g. the relational or UML metamodel).

We propose that generic model management should employ a generic metamodel (GMM) which serves as an abstraction of particular metamodels and preserves as much of the original features of modeling constructs as possible. A naive generalization of the elements of concrete metamodels in generic metaclasses would lose some of the specific features of the metamodels, or yield a prohibitive number of metaclasses in the GMM. To avoid these problems, we propose the Generic Role based Metamodel GeRoMe in which each model element is decorated with a set of role objects that represent specific properties of the model element. Roles may be added to or removed from elements at any time, which enables a very flexible and dynamic yet accurate definition of models.

Roles expose to operators different views on the same model element. Thus, operators concentrate on features which affect their functionality but may remain agnostic about other features. Consequently, these operators can use polymorphism and have to be implemented only once using GeRoMe, and not for each specific metamodel. We verified our results by implementing GeRoMe and a selection of model management operators using our metadata system ConceptBase.

Keywords

Model Management Generic Role Export Operator Role Class Very Large Data Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • David Kensche
    • 1
  • Christoph Quix
    • 1
  • Mohamed Amine Chatti
    • 1
  • Matthias Jarke
    • 1
    • 2
  1. 1.RWTH Aachen University, Informatik V (Information Systems), 52056 AachenGermany
  2. 2.Fraunhofer FIT, Schloss Birlinghoven, 53574 St. AugustinGermany

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