BWiWi 1.14 – Foundations of Decision Support Systems

Date:

lecture: Monday, 2:00  - 4:00 p.m., lecture hall 14 (M.10.12), begin: April 7, 2025

exercise: Tuesday, 10:00 - 12:00 a.m., lecture hall 32 (K.11.23), begin April, 15, 2024

Moodle-Course:

Course: Grundlagen von Decision Support Systemen 

  • Key: GDSS2025

Lecturers: Prof. Dr. Stefan Bock, Cedric Leon Renner (M. Sc.)

 

Qualification goals:


Students have a comprehensive understanding of the mathematical and algorithmic foundations of database systems, of methods for data acquisition in the context of forecasting systems and for optimisation. They have an understanding of the mathematical structures considered in each case, their fundamentals and the algorithms used. Students have a basic understanding of data management and optimisation problems in operational applications of operations management. They know basic definitions of terms in business informatics and operations research. Students have a basic awareness of problems and can use mathematical modelling and solution procedures for previously motivated problems to assess the ability to select suitable methods for generating, maintaining and using data. Students are able to analyse the solution of the problems considered in the areas of database systems, forecasting systems and optimisation across problems and can develop algorithms through conceptual thinking. After completing the module, students fulfil the prerequisites for successfully completing further in-depth modules in the field of information and data management (knowledge-based systems, data organisation) and operations research.

Contents:

 

  • Fundamentals (basic terms, categorisation of the course and the interdisciplinary research area)
  • Database systems (data management, data models, ER model, relational model, relational algebra, design theory (normal forms)
  • Determination of forecast data (qualitative forecasting, causal forecasting, time series forecasting, assessment of forecast quality)
  • Introduction to optimization (basics of linear programming, stochastic programming using the newsvendor problem)