Inhalt

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

Versionsauswahl
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 http://www.cis.jku.at
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