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

5 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

Fingerprint

Domain-specific Languages
Metamodel
Information retrieval
Ecosystem
Information Retrieval
Repository
Ecosystems
Learning systems
Scalability
Machine Learning
Industry
Engineering
Model
Evidence

Keywords

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

Cite this

Babur, Ö., Cleophas, L., van den Brand, M. G. J., Tekinerdogan, B., & Aksit, M. (2018). Models, more models, and then a lot more. In M. Seidl, & S. Zschaler (Eds.), Software Technologies: Applications and Foundations - STAF 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017: revised selected papers (pp. 129-135). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10748 LNCS). Cham: Springer. https://doi.org/10.1007/978-3-319-74730-9_10
Babur, Ö. ; Cleophas, L. ; van den Brand, M.G.J. ; Tekinerdogan, B. ; Aksit, M. / Models, more models, and then a lot more. Software Technologies: Applications and Foundations - STAF 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017: revised selected papers. editor / M. Seidl ; S. Zschaler. Cham : Springer, 2018. pp. 129-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{12a56abd193c458e937908201ebd580e,
title = "Models, more models, and then a lot more",
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.",
keywords = "Data mining, Machine learning, Model analytics, Model-Driven Engineering, Scalability",
author = "{\"O}. Babur and L. Cleophas and {van den Brand}, M.G.J. and B. Tekinerdogan and M. Aksit",
year = "2018",
doi = "10.1007/978-3-319-74730-9_10",
language = "English",
isbn = "978-3-319-74729-3",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "129--135",
editor = "M. Seidl and S. Zschaler",
booktitle = "Software Technologies",
address = "Germany",

}

Babur, Ö, Cleophas, L, van den Brand, MGJ, Tekinerdogan, B & Aksit, M 2018, Models, more models, and then a lot more. in M Seidl & S Zschaler (eds), Software Technologies: Applications and Foundations - STAF 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017: revised selected papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10748 LNCS, Springer, Cham, pp. 129-135, 2017 International conference on Software Technologies: Applications and Foundations (STAF 2017), Marburg, Germany, 17/07/17. https://doi.org/10.1007/978-3-319-74730-9_10

Models, more models, and then a lot more. / Babur, Ö.; Cleophas, L.; van den Brand, M.G.J.; Tekinerdogan, B.; Aksit, M.

Software Technologies: Applications and Foundations - STAF 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017: revised selected papers. ed. / M. Seidl; S. Zschaler. Cham : Springer, 2018. p. 129-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10748 LNCS).

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

TY - GEN

T1 - Models, more models, and then a lot more

AU - Babur, Ö.

AU - Cleophas, L.

AU - van den Brand, M.G.J.

AU - Tekinerdogan, B.

AU - Aksit, M.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Data mining

KW - Machine learning

KW - Model analytics

KW - Model-Driven Engineering

KW - Scalability

UR - http://www.scopus.com/inward/record.url?scp=85042682091&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-74730-9_10

DO - 10.1007/978-3-319-74730-9_10

M3 - Conference contribution

AN - SCOPUS:85042682091

SN - 978-3-319-74729-3

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 129

EP - 135

BT - Software Technologies

A2 - Seidl, M.

A2 - Zschaler, S.

PB - Springer

CY - Cham

ER -

Babur Ö, Cleophas L, van den Brand MGJ, Tekinerdogan B, Aksit M. Models, more models, and then a lot more. In Seidl M, Zschaler S, editors, Software Technologies: Applications and Foundations - STAF 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017: revised selected papers. Cham: Springer. 2018. p. 129-135. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-74730-9_10