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A Generic and Customizable Genetic Algorithms-Based Conceptual Model Modularization Framework

  • Conference paper
  • First Online:
Enterprise Design, Operations, and Computing (EDOC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14367))

  • 211 Accesses

Abstract

Conceptual models need to be comprehensible and maintainable by humans to exploit their full value in faithfully representing a subject domain. Modularization, i.e. breaking down the monolithic model into smaller, comprehensible chunks has proven very valuable to maintain this value even for very large models. The quality of modularization however often depends on application-specific requirements, the domain, and the modeling language. A well-defined generic modularizing framework applicable to different modeling languages and requirements is lacking. In this paper, we present a customizable and generic multi-objective conceptual models modularization framework. The multi-objective aspect supports addressing heterogeneous requirements while the framework’s genericity supports modularization for arbitrary modeling languages and its customizability is provided by adopting the modularization configuration up to the level of using user-defined heuristics. Our approach applies genetic algorithms to search for a set of optimal solutions. In this paper, we present the details of our Generic Genetic Modularization Framework with a case study to show i) the feasibility of our approach by modularizing models from multiple modeling languages, ii) the customizability by using different objectives for the modularization quality, and, finally, iii) a comparative performance evaluation of our approach on a dataset of ER and ECore models.

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Notes

  1. 1.

    Note, we use graph clustering/partitioning and modularization interchangeably in this paper.

  2. 2.

    https://github.com/me-big-tuwien-ac-at/GGMF

  3. 3.

    https://zenodo.org/record/2585456#.YM5ziSbtb0o.

  4. 4.

    https://drawsql.app/templates.

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Authors and Affiliations

  1. TU Wien, Business Informatics Group, Vienna, Austria

    Syed Juned Ali, Jan Michael Laranjo & Dominik Bork

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  1. Syed Juned Ali
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  2. Jan Michael Laranjo
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  3. Dominik Bork
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Corresponding author

Correspondence to Syed Juned Ali .

Editor information

Editors and Affiliations

  1. Vienna University of Technology, Vienna, Austria

    Henderik A. Proper

  2. Technical University of Berlin, Berlin, Germany

    Luise Pufahl

  3. University of Groningen, Groningen, The Netherlands

    Dimka Karastoyanova

  4. University of Twente, Enschede, The Netherlands

    Marten van Sinderen

  5. University of Twente, Enschede, The Netherlands

    João Moreira

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Ali, S.J., Michael Laranjo, J., Bork, D. (2024). A Generic and Customizable Genetic Algorithms-Based Conceptual Model Modularization Framework. In: Proper, H.A., Pufahl, L., Karastoyanova, D., van Sinderen, M., Moreira, J. (eds) Enterprise Design, Operations, and Computing. EDOC 2023. Lecture Notes in Computer Science, vol 14367. Springer, Cham. https://doi.org/10.1007/978-3-031-46587-1_3

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  • DOI: https://doi.org/10.1007/978-3-031-46587-1_3

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