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                      | Detailed information |  
                      | Original study plan | Master's programme Computer Science 2023W |  
                      | Objectives | Graduates 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. |  
                      | Subject | 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
Modelmanagement (Interchange, Persistency, Comparison, Versioning, Co-Evolution, Quality, Verification and Testing)
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                      | Criteria for evaluation | Exam and presentations of students |  
                      | Methods | Slide-based Lecture and student presentations (work in groups) |  
                      | Language | German or English, depending on the participants |  
                      | Study material | 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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                      | Changing subject? | No |  
                      | Further information | http://www.cis.jku.at |  
                      | Earlier variants | They also cover the requirements of the curriculum (from - to) 921CGELAMEK13: KV Advanced Model Engineering (2013W-2023S)
 
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