Leveraging Model-Based Tool Integration by Conceptual Modeling Techniques

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In the context of model-based tool integration, model transformation languages are the first choice for realizing model exchange between heterogenous tools. However, the lack of a conceptual view on the integration problem and appropriate reuse mechanisms for already existing integration knowledge forces the developer to define model transformation code again and again for certain recurring integration problems in an implementation-oriented manner resulting in low productivity and maintainability of integration solutions. In this chapter, we summarize our work on a framework for model-based tool integration which is based on well-established conceptual modeling techniques. It allows to design integration models on a conceptual level in terms of UML component diagrams. Not only the design-time is supported by conceptual models, but also the runtime, i.e., the execution of integration models, is represented by conceptual models in terms of Coloured Petri Nets. Furthermore, we show how reusable integration components for resolving structural metamodel heterogeneities, which are one of the most frequently recurring integration problems, can be implemented within our framework.
Original languageEnglish
Title of host publicationThe Evolution of Conceptual Modeling
Subtitle of host publicationFrom a Historical Perspective towards the Future of Conceptual Modeling
EditorsRoland Kaschek, Lois Delcambre
PublisherSpringer Verlag Berlin-Heidelberg
Pages254-284
Number of pages31
ISBN (Print)9783642175046
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6520
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of science

  • 106013 Genetics
  • 106041 Structural biology
  • 102 Computer Sciences
  • 101029 Mathematical statistics
  • 102001 Artificial intelligence
  • 101004 Biomathematics
  • 102015 Information systems
  • 102018 Artificial neural networks
  • 106002 Biochemistry
  • 106023 Molecular biology
  • 305 Other Human Medicine, Health Sciences
  • 106005 Bioinformatics

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