Metamodelling: State of the art and research challenges

Jonathan Sprinkle, Bernhard Rumpe, Hans Vangheluwe, Gabor Karsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

Abstract

This chapter discusses the current state of the art, and emerging research challenges, for metamodelling. In the state-of-the-art review on metamodelling, we review approaches, abstractions, and tools for metamodelling, evaluate them with respect to their expressivity, investigate what role(s) metamodels may play at run-time and how semantics can be assigned to metamodels and the domain-specific modeling languages they could define. In the emerging challenges section on metamodelling we highlight research issues regarding the management of complexity, consistency, and evolution of metamodels, and how the semantics of metamodels impacts each of these.

Original languageEnglish (US)
Title of host publicationModel-Based Engineering of Embedded Real-Time Systems - International Dagstuhl Workshop, Revised Selected Papers
Pages57-76
Number of pages20
DOIs
StatePublished - Nov 23 2010
EventInternational Dagstuhl Workshop on Model-Based Engineering of Embedded Real-Time Systems - Dagstuhl Castle, Germany
Duration: Nov 4 2010Nov 9 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6100 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Dagstuhl Workshop on Model-Based Engineering of Embedded Real-Time Systems
CountryGermany
CityDagstuhl Castle
Period11/4/1011/9/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Sprinkle, J., Rumpe, B., Vangheluwe, H., & Karsai, G. (2010). Metamodelling: State of the art and research challenges. In Model-Based Engineering of Embedded Real-Time Systems - International Dagstuhl Workshop, Revised Selected Papers (pp. 57-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6100 LNCS). https://doi.org/10.1007/978-3-642-16277-0_3