Models, more models, and then a lot more

Ö. Babur, L. Cleophas, M.G.J. van den Brand, B. Tekinerdogan, M. Aksit

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

    7 Citations (Scopus)
    1 Downloads (Pure)

    Abstract

    With increased adoption of Model-Driven Engineering, the number of related artefacts in use, such as models, metamodels and transformations, greatly increases. To confirm this, we present quantitative evidence from both academia — in terms of repositories and datasets — and industry — in terms of large domain-specific language ecosystems. To be able to tackle this dimension of scalability in MDE, we propose to treat the artefacts as data, and apply various techniques — ranging from information retrieval to machine learning — to analyse and manage those artefacts in a holistic, scalable and efficient way.

    Original languageEnglish
    Title of host publicationSoftware Technologies
    Subtitle of host publicationApplications and Foundations - STAF 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017: revised selected papers
    EditorsM. Seidl, S. Zschaler
    Place of PublicationCham
    PublisherSpringer
    Pages129-135
    Number of pages7
    ISBN (Print)978-3-319-74729-3
    DOIs
    Publication statusPublished - 2018
    Event2017 International conference on Software Technologies: Applications and Foundations (STAF 2017) - Marburg, Germany
    Duration: 17 Jul 201721 Jul 2017
    http://www.informatik.uni-marburg.de/staf2017/

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10748 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference2017 International conference on Software Technologies: Applications and Foundations (STAF 2017)
    Abbreviated titleSTAF 2017
    CountryGermany
    CityMarburg
    Period17/07/1721/07/17
    Internet address

    Keywords

    • Data mining
    • Machine learning
    • Model analytics
    • Model-Driven Engineering
    • Scalability

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