J. Meier, H. Klare, C. Tunjic, C. Atkinson, E. Burger, R. Reussner, and A. Winter, "Single Underlying Models for Projectional , Multi-View Environments" Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2019).
@article{Meier2019,
author = {Meier, Johannes and Klare, Heiko and Tunjic, Christian and Atkinson, Colin and Burger, Erik and Reussner, Ralf and Winter, Andreas},
editor = {Hammoudi, Slimane and Pires, Luis Ferreira and Selic, Bran},
file = {:Users/johannes/Documents/beruf/svn/projects/website/content/documents/MeierKlare+2019.pdf:pdf},
isbn = {978-989-758-358-2},
journal = {Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2019)},
pages = {119--130},
publisher = {SCITEPRESS},
title = {{Single Underlying Models for Projectional , Multi-View Environments}},
year = {2019}
}
C. Atkinson and T. Kühne, "The Essence of Multilevel Metamodeling" in Proc. ≪UML≫ 2001 --- The Unified Modeling Language. Modeling Languages, Concepts, and Tools, Berlin, Heidelberg, 2001.
@InProceedings{10.1007/3-540-45441-1_3,
author="Atkinson, Colin and K{\"u}hne, Thomas", editor="Gogolla, Martin and Kobryn, Cris", title="The Essence of Multilevel Metamodeling", booktitle="≪UML≫ 2001 --- The Unified Modeling Language. Modeling Languages, Concepts, and Tools", year="2001", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="19--33", abstract="As the UMLattempts to make the transition from a single, albeit extensible, language to a framework for a family of languages, the nature and form of the underlying meta-modeling architecture will assume growing importance. It is generally recognized that without a simple, clean and intuitive theory of how metamodel levels are created and related to one another, the UML2.0 vision of a coherent family of languages with a common core set of concepts will remain elusive. However, no entirely satisfactory metamodeling approach has yet been found. Current (meta-)modeling theories used or proposed for the UML all have at least one fundamental problem that makes them unsuitable in their present form. In this paper we bring these problems into focus, and present some fundamental principles for overcoming them. We believe that these principles need to be embodied within the metamodeling framework ultimately adopted for the UML2.0 standard.", isbn="978-3-540-45441-0" }
C. Atkinson and T. Kühne, "In defence of deep modelling" Information and Software Technology, vol. 64.
doi: 10.1016/j.infsof.2015.03.010
@article{ATKINSON201536, title = "In defence of deep modelling", journal = "Information and Software Technology", volume = "64", pages = "36 - 51", year = "2015", issn = "0950-5849", doi = "https://doi.org/10.1016/j.infsof.2015.03.010", url = "http://www.sciencedirect.com/science/article/pii/S0950584915000671",
author = "Colin Atkinson and Thomas Kühne", keywords = "Multi-level modelling, Deep modelling, Metamodelling, Ontological classification, Clabjects", abstract = "Context Since multi-level modelling emerged as a strategy for leveraging classification levels in conceptual models, there have been discussions about what it entails and how best to support it. Recently, some authors have claimed that the deep modelling approach to multi-level modelling entails paradoxes and significant weaknesses. By drawing upon concepts from speech act theory and foundational ontologies these authors argue that hitherto accepted principles for deep modelling should be abandoned and an alternative approach be adopted instead (Eriksson et al., 2013). Objective We investigate the validity of these claims and motivate the need to shift the focus of the debate from philosophical arguments to modelling pragmatics. Method We present each of the main objections raised against deep modelling in turn, classify them according to the kinds of arguments put forward, and analyse the cogency of the supporting justification. We furthermore analyse the counter proposal regarding its pragmatic value for modellers. Results Most of the criticisms against deep modelling are based on mismatches between the premisses used in published definitions of deep modelling and those used by the authors as the basis of their challenges. Hence, most of the criticisms levelled at deep modelling do not actually apply to deep modelling as defined in the literature. We also explain how the proposed alternative introduces new problems of its own, and evaluate its merits from a pragmatic modelling perspective. Finally, we show how deep modelling is indeed compatible with, and can be founded on, classic work in linguistics and logic. Conclusions The inappropriate interpretations of the core principles of deep modelling identified in this article indicate that previous descriptions of them have not had sufficient clarity. We therefore provide further clarification and foundational background material to reduce the chance for future misunderstandings and help establish deep modelling as a solid foundation for multi-level modelling." }
C. Atkinson and T. Kühne, "Demystifying Ontological Classification in Language Engineering" in Proc. Modelling Foundations and Applications, Cham, 2016.
@InProceedings{10.1007/978-3-319-42061-5_6,
author="Atkinson, Colin and K{\"u}hne, Thomas", editor="W{\k{a}}sowski, Andrzej and L{\"o}nn, Henrik", title="Demystifying Ontological Classification in Language Engineering", booktitle="Modelling Foundations and Applications", year="2016", publisher="Springer International Publishing", address="Cham", pages="83--100", abstract="The introduction of ontological classification to support domain-metamodeling has been pivotal in the emergence of multi-level modeling as a dynamic research area. However, existing expositions of ontological classification have only used a limited context to distinguish it from the historically more commonly used linguistic classification. In important areas such as domain-specific languages and classic language engineering the distinction can appear to become blurred and the role of ontological classification is obscured, if not fundamentally challenged. In this paper we therefore examine critical points of confusion regarding the distinction and provide an expanded explanation of the differences. We maintain that optimally utilizing ontological classification, even for tasks that traditionally have only been viewed as language engineering, is critical for mastering the challenges in complex systems modeling including the validation of multi-language models.", isbn="978-3-319-42061-5" }
