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 language | English |
|---|---|
| Title of host publication | Software Technologies |
| Subtitle of host publication | Applications and Foundations - STAF 2017 Collocated Workshops, Revised Selected Papers |
| Editors | Steffen Zschaler, Martina Seidl |
| Publisher | Springer |
| Pages | 129-135 |
| Number of pages | 7 |
| ISBN (Print) | 9783319747293 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | Software Technologies: Applications and Foundations, STAF 2017 - Technologie- und Tagungszentrum Marburg (TTZ), Marburg, Germany Duration: 17 Jul 2017 → 21 Jul 2017 http://www.informatik.uni-marburg.de/staf2017/ |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10748 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | Software Technologies: Applications and Foundations, STAF 2017 |
|---|---|
| Abbreviated title | STAF 2017 |
| Country/Territory | Germany |
| City | Marburg |
| Period | 17/07/17 → 21/07/17 |
| Internet address |
Keywords
- Data mining
- Machine learning
- Model analytics
- Model-Driven Engineering
- Scalability
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