Learning outcomes

By the end of this course, students will be able to:

  1. Understand the purpose and key challenges of information systems modelling.

  2. Understand the basic concepts of information systems modelling.

  3. Define the concept of data modelling and explain its importance for information systems.

  4. Define and compare three types of data models: conceptual, logical, and physical.

  5. Based on requirements expressed in natural language, create an Extended Entity-Relationship (EER) model representing the data layer of an information system, using Draw.io and on paper.

  6. Transform an Extended Entity-Relationship model into a normalized logical relational model, represented on paper and using DBML.

  7. Justify design choices when creating and transforming models where several alternatives are possible.

  8. Assess the quality of a data model.

  9. Select, with justification, between several alternative data models for a given information system.

  10. Normalize relational models.

  11. Master the basic concepts of data governance and data quality.

  12. Develop information systems models using UML diagrams.

  13. Integrate different UML models into complex projects.

  14. Use modelling tools such as Draw.io and DBML.

Goals

The objectives of this course are:

  1. To provide an in-depth understanding of information systems and data modelling techniques.

  2. To develop the skills required to use modelling tools to create and manage information systems models.

  3. To prepare students to analyse and improve the quality of data models and their governance.

  4. To train students in the use of UML, Entity-Relationship, and relational diagrams for the design of complex information systems.

  5. To enable students to manage an information system specification project by integrating its different layers, including data, behaviour, and interface, and to introduce them to the fundamentals of prototyping as a non-notation-based form of modelling.

Content

  1. General introduction to information systems modelling, Requirements Engineering, and Business Analysis.

  2. Use case diagrams.

  3. Activity diagrams.

  4. Conceptual Data Modelling (Extended Entity-Relationship language).

  5. Logical and Physical Data Modelling (relational language, conversion, and normalization).

  6. Class diagrams.

  7. State diagrams.

  8. Sequence diagrams.

  9. Introduction to prototyping (“vibe coding” approach).

Teaching methods

• Lectures: Presentation of theoretical concepts.
• Practical Sessions: Complex and in-depth modelling exercises, with particular attention to model quality and governance.
• Tools: Use of Draw.io and DBML.

Assessment method

·      Examen (70%) : Évaluation finale écrite avec des questions et exercices théoriques et pratiques ainsi qu'une étude de cas.

·      Projet (30%) : Réalisation en groupe d'un projet de spécification et de modélisation d'un système d'information (incluant un rapport intermédiaire), évalué sur la qualité des modèles produits et leur cohérence avec les exigences, ainsi que lors d’une défense orale.

Sources, references and any support material

  • Charroux, B., Osmani, A., & Thierry-Mieg, Y. (2010). UML 2: pratique de la modélisation. Paris: Pearson Education.
  • Roques, P. (2018). UML 2.5 par la pratique: Etudes de cas et exercices corrigés. Editions Eyrolles.
  • https://www.uml.org
  • PlantUML
  • DBML
  • Snoeck, M. (2014). Enterprise information systems engineering. The MERODE Approach. Springer
  • Allen, S. L., & Terry, E. (2006). Beginning relational data modeling. Apress.
  • Simsion, G., & Witt, G. (2004). Data modeling essentials. Elsevier.

Language of instruction

French
Training Block Credits Mandatory
Bachelor in Business Engineering 3 3 Yes