Surviving the Heterogeneity Jungle with Composite Mapping Operators

Manuel Wimmer, Gertrude Kappel, Angelika Kusel, Werner Retschitzegger, Johannes Schönböck, Wieland Schwinger

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

Model transformations play a key role in the vision of Model Driven Engineering. Nevertheless, mechanisms like abstraction, variation and composition for specifying and applying reusable model transformations { like urgently needed for resolving recurring structural heterogeneities { are insufficiently supported so far. Therefore, we propose to specify model transformations by a set of pre-defined mapping operators (MOps), each resolving a certain kind of structural heterogeneity. Firstly, these MOps can be used in the context of arbitrary metamodels since they abstract from concrete metamodel types. Secondly, MOps can be tailored to resolve certain structural heterogeneities by means of black-box reuse. Thirdly, based on a systematic set of kernel MOps resolving basic heterogeneities, composite ones can be built in order to deal with more complex scenarios. Finally, an extensible library of MOps is proposed, allowing for automatically executable mapping specifications since every MOp exhibits a clearly defined operational semantics.
Original languageEnglish
Title of host publicationTheory and Practice of Model Transformations - Third International Conference, ICMT 2010, Proceedings
PublisherSpringer
Pages260-275
Number of pages16
ISBN (Print)3642136877, 9783642136870
DOIs
Publication statusPublished - Jan 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6142 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of science

  • 102 Computer Sciences
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  • 202038 Telecommunications
  • 101004 Biomathematics
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  • 101015 Operations research
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  • 101024 Probability theory
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  • 102001 Artificial intelligence
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  • 102018 Artificial neural networks
  • 102019 Machine learning
  • 103029 Statistical physics
  • 106005 Bioinformatics
  • 106007 Biostatistics
  • 202017 Embedded systems
  • 202035 Robotics
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  • 202037 Signal processing
  • 305901 Computer-aided diagnosis and therapy
  • 305905 Medical informatics
  • 305907 Medical statistics
  • 102032 Computational intelligence
  • 102033 Data mining
  • 101031 Approximation theory

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