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| Detailed information |
| Pre-requisites |
(*)Erwartete Vorkenntnisse: Grundlagen der Wirtschaftsinformatik, Grundlagen der Informatik, Grundlagen der Mathematik, Statistik und formaler Methoden
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| Original study plan |
Bachelor's programme Business Informatics 2025W |
| Learning Outcomes |
Competences |
| Students are able to design and implement data- and knowledge-based systems taking into account the current state of science.
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| Skills |
Knowledge |
- LO2: Students understand the structure, functionality and special features of database systems based on various data models, web-based information systems and systems based on symbolic or sub-symbolic methods of artificial intelligence (K2).
- LO3: The can differentiate their typical areas of application (K4).
- LO4: They can apply methods and techniques of data and knowledge engineering (K3) and can explain distributed, temporal, object-relational and non-relational concepts for the design of database systems (K2).
- LO5: They are proficient in the exemplary use of object-relational and non-relational database systems (K3) as well as elementary techniques of data warehousing and data mining (K1).
- LO6: They can explain concepts of knowledge-based systems (K2), apply selected methods of knowledge-based systems (K3) and recognize typical areas of application of ontologies and knowledge graphs as well as business rule engines (K3) and use them as examples (K3).
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LO1: Functionality of database management systems: Multi-user control, recovery; Database technology: distributed database systems, object-relational database systems, temporal database systems, NoSQL and NewSQL database systems, document-oriented database systems, active database systems and business rule engines, deductive database systems; Fundamentals of symbolic and sub-symbolic artificial intelligence: Knowledge Representation and Logical Reasoning, Heuristic Search, Constraint Satisfication, Answer Set Programming, Genetic Algorithms, Neural Networks; Web-based Information Systems: Web Query Languages, Use of Semistructured Data on the Web, Semantic Web/Web of Data. Business Intelligence and Analytics: Data Warehousing, OLTP vs OLAP, Dimensional Fact Model; Data Mining, Decision Tree Learning, Association Rules.
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| Criteria for evaluation |
midterm and final exam
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| Methods |
A characteristic feature of the teaching method used here is working with an e-tutor system. The topics covered in the lecture are deepened in the exercise by working on practical examples. These examples are worked on by the students using suitable tools under the guidance of the tutor. The exercises are completed online with the help of the electronic tutoring system eTutor. The eTutor system assists students in finding and correcting errors in the editing process and automatically evaluates the solution. In addition, the automatic assessments are checked by the course instructors. The final assessment is carried out by the course instructor.
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| Language |
German |
| Study material |
Core Reading:
- Garcia-Molina, H.; Ullman, J. D.; Widom, J.: Database Systems – The Complete Book. Prentice Hall, newest edition.
- Russell, S; Norvig, P.: Artificial Intelligence. International Version. Addison Wesley, newest edition.
- Kemper, S.; Eickler, A.: Datenbank Systeme: Eine Einführung. Oldenbourg Verlag, in der aktuellen Auflage.
Supplementary material will be announced each semester.
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| Changing subject? |
No |
| Further information |
VL and UE Data & Knowledge Engineering form an inseparable didactic unit. The learning outcomes described are achieved through the interaction of both.
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
2WDEV: VL Data & Knowledge Engineering (2002W-2014S)
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