[ 921CGELMEDK23 ] KV Model Engineering for Data-Intensive Systems

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Computer Science Werner Retschitzegger 2 hpw Johannes Kepler University Linz
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

  1. UML2 – selected topics and modeling heuristics
  2. Metamodeling (MOF, Ecore/EMF)
  3. Model-to-Model Transformations (OCL, ATL as industrial-strength realisation of QVT)
  4. Model-to-Code Transformations (XML-based, Java-based, Model-based)
  5. Commonalities and Differences between Model Engineering and Low-Code Development

Model Engineering Specifics for Data-Intensive Systems

  1. Development of Domain-specific Languages (DSL)
  2. Model Engineering Techniques for Forward Engineering (Schema-First) and Reverse Engineering from Data and Code (Schema-on-Read)
  3. Design Patterns and Design Heuristics
  4. Modelmanagement (Interchange, Persistency, Comparison, Versioning, Co-Evolution, Quality, Verification and Testing)
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
Changing subject? No
Further information
Earlier variants They also cover the requirements of the curriculum (from - to)
921CGELAMEK13: KV Advanced Model Engineering (2013W-2023S)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment