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| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2025W |
| Lernergebnisse |
Kompetenzen |
| (*)Students understand the concepts and techniques of model engineering in general and for data-intensive systems in particular. They are capable of developing data-intensive systems on basis of model engineering techniques and have knowledge about specific applications and current trends in model engineering.
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| Fertigkeiten |
Kenntnisse |
(*)Students are able to
- understand the principles of model engineering (MDE) and low-code development (LCD) (K2)
- analyze and evaluate existing forward- and reverse engineering techniques (K4, K5)
- apply MDE and LCD techniques to data-intensive systems (K3)
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(*)Principles of Model Engineering
- UML2 – selected topics and modeling heuristics
- Metamodeling (MOF, Ecore/EMF)
- Model-to-Model Transformations (OCL, ATL as industrial-strength realisation of QVT)
- Model-to-Code Transformations (XML-based, Java-based, Model-based)
- Commonalities and Differences between Model Engineering and Low-Code Development
Model Engineering Specifics for Data-Intensive Systems
- Development of Domain-specific Languages (DSL)
- Model Engineering Techniques for Forward Engineering (Schema-First) and Reverse Engineering from Data and Code (Schema-on-Read)
- Design Patterns and Design Heuristics
- Model management (Interchange, Persistency, Comparison, Versioning, Co-Evolution, Quality, Verification and Testing)
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| Beurteilungskriterien |
(*)Exam and presentations of students
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| Lehrmethoden |
(*)Slide-based Lecture and student presentations (work in groups)
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| Abhaltungssprache |
Deutsch oder Englisch, abhängig von TeilnehmerInnen |
| Literatur |
(*)- M. Hitz, G. Kappel, E. Kapsammer, W. Retschitzegger, "UML@ Work", dpunkt, 2005
- M. Brambilla et al., "Model-Driven Software Engineering in Practice", Morgan & Claypool, 2012
- M. Kleppmann, “Designing Data-Intensive Applications – The Big Ideas Behind Reliable, Scalable, and Maintainable Systems”, O'Reilly, March 2017
- G. Mussbacher, et al., "A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems", in IEEE Software, volume 38, issue 4, pages 71-84, July 2021
- A. Moin, M. Challenger, A. Badii, S. Günnemann, "A model-driven approach to machine learning and software modeling for the IoT“, Software and Systems Modeling (2022) 21:987–1014
- A. C. Bock and U. Frank, "In Search of the Essence of Low-Code: An Exploratory Study of Seven Development Platforms“, 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Fukuoka, Japan, 2021, pp. 57-66
- F. Melchor, R. Rodriguez-Echeverria, J.M. Conejero, Á.E. Prieto, J.D. Gutiérrez, A Model-Driven Approach for Systematic Reproducibility and Replicability of Data Science Projects, in: X. Franch, G. Poels, F. Gailly, M. Snoeck (eds), Advanced Information Systems Engineering, CAiSE 2022, Lecture Notes in Computer Science, vol 13295. Springer
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| Lehrinhalte wechselnd? |
Nein |
| Sonstige Informationen |
(*)http://www.cis.jku.at
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| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
921CGELAMEK13: KV Advanced Model Engineering (2013W-2023S)
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