Data Modelling in Engineering
1. Knowledge: a) Base modeling concepts and their applicability to engineering. b) Becoming familiar with various modeling formalisms.
2. Know-how: a) Capacity to model small systems. b) Abstract modeling skills.
3. Non-technical skills: a) Team work. b) Time management.
Luís Manuel Camarinha de Matos
Weekly - 4
Total - 63
1. Course notes - L.M. Camarinha Matos
2. The essence of databases - F. D. Rolland, Prentice Hall, 1998, ISBN 0-13-727827-6
3. AI through Prolog . Neil C. Rowe, Prentice Hall, ISBN 0-13-049362-7.
4. UML for Systems Engineering: Watching the Wheels, Jon Holt , 2001, ISBN:0852961057.
The course includes a theoretical-practical component and a laboratory component. The theoretical-practical component is provided through formal lectures on the concepts proposed in the program, complemented with exercises. The course has continuous assessment, by conducting evaluation tests carried out during the semester.
The practical component is provided through laboratory work, supported by teaching staff, where students work in groups to solve practical problems within this area. The evaluation is made on the results obtained in these works.
The rating is given by the average of the two evaluation components.
3 Mini-tests - 60% (25%, 20%, 15%)
3 Lab Works - 40% (15%, 15%, 10%)
Minimum score: Tests >=9.5 Lab works >= 9.5
|2.||MODELING BASED ON RELATIONAL MODEL
|3.||MODELING BASED ON LOGIC PROGRAMMING
|4.||MODELING BASED ON FRAMES
|5.||GRAPHICAL LANGUAGES - UML|
|6.||INTRODUCTION TO ONTOLOGIES|
Programs where the course is taught: