Models, More Models, and Then a Lot More

Önder Babur*, Loek Cleophas, Mark van den Brand, Bedir Tekinerdogan, Mehmet Aksit

*Corresponding author for this work

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

    7 Citations (Scopus)

    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, Revised Selected Papers
    EditorsSteffen Zschaler, Martina Seidl
    PublisherSpringer Verlag
    Pages129-135
    Number of pages7
    ISBN (Print)9783319747293
    DOIs
    Publication statusPublished - 2018
    EventSoftware Technologies: Applications and Foundations - Technologie- und Tagungszentrum Marburg (TTZ), 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

    ConferenceSoftware Technologies: Applications and Foundations
    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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