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Database and Knowledge-based Systems (E371079)
Departments:ústav přístrojové a řídící techniky (12110)
Valid until: ??Range:3+1
Basic data models. Types and examples of database systems. Management of database systems. Design of database systems - examples. Programming techniques. Language SQL. Fundamentals of programming in database systems MS ACCESS and MySQL.
Introduction in knowledge-based systems. Examples of applying knowledge-based systems in Engineering. Knowledge-based systems developed on principles of formal logic. Principles of Prolog. Knowledge-based systems with uncertainty calculus. Formal description of uncertainty. Fuzzy set theory. Computations with fuzzy sets. Linguistic approximation. Fuzzy logic. Types of fuzzy logic implications and inferences. Rule-based systems. Expert systems - modular structure. Examples of expert systems: Expert System Builder, ETS, TRACER. Principles of Data-mining of knowledge from databases. Concept lattices. Hasse diagram. Rough sets.
prof. Ing. Jiří Bíla DrSc.
Letní 2018/2019
Ing. Vladimír Hlaváč Ph.D.
Letní 2018/2019
prof. Ing. Jiří Bíla DrSc.
Letní 2017/2018
Ing. Vladimír Hlaváč Ph.D.
Letní 2017/2018
prof. Ing. Jiří Bíla DrSc.
Letní 2016/2017
Ing. Vladimír Hlaváč Ph.D.
Letní 2016/2017
P1. Introduction. General operations with information provided by data-base systems.
P2. E - R conceptual model. Limitations for relations defined in DBS.
P3. Models of Data-base System Management. Relational data-base systems. Codd, Characteristics (rules) for relational data-base systems. Set and relation operations in, relation data-base system.
P4. Architectures of Database System Management in personal computers. Systems, "client/server".
P5. Data-base application program languages. Structured language - SQL.
P6. Programming in the system MS Access.
P7. Programming in MySQL
P8. Data-based and Knowledge-Based systems. Operations with knowledge. Operations with uncertainties. Theory of fuzzy sets., Operations with fuzzy sets. Fuzzy numbers and computing with fuzzy numbers., Linguistic variable.
P9. Fuzzy logic. Compositional rule and fuzzy inference. Types of fuzzy implications and, their properties.
P10. Rule-based systems. Expert system - modular structure., Inference engine and its co-operations with knowledge-base.
P11. Expert systems examples - Expert System Builder, System ETS, TRACER.
P12. Examples of expert system applications - Instruction systems for complex devices, Multicriteria optimisation, Diagnostics.
P13. Principles of Data-mining knowledge from databases. Data Mining Context. Concept lattices.
P14. Hasse diagram. Extraction of rules. Examples. Rough sets. Conclusions of lectures.
Structure of tutorial
1. Fundamentals of work with Data-base systems.
2. Data-based systems for non-programmers.
3. Introduction in data-base system MS ACCESS. Realisation of set and relational operations.
4. Structured language - SQL. Assignment of semester tasks.
5. Programming in MS ACCESS.
6. Programming in MySQL.
7. Programming in ACCESS versus programming of SQL based system.
8. Fuzzy numbers, computing with fuzzy numbers.
9. Fuzzy logic. Compositional rule and fuzzy inference. Types of fuzzy implications and their properties.
10. Rule-based systems. Expert systems: Expert System Builder. Expert System ETS. Assignment of semester tasks.
11. Expert system ETS - examples. Expert system NEST.
12. Assistance and testing of results of semester tasks.
13. Data mining techniques. Concept lattices. Hasse diagram. Extraction of rules.
14. Testing of semester tasks. Conclusion of the semester.
1. Oppel, Andy: Databases. A Beginner's Guide. The McGraw Hill Companies, 2009. (recommended)
2. ISRD Group: Introduction to Database Management Systems. Tata McGraw-Hill Education, 2006. (available on the Google books, try ww.google.cz/?q=tata+radqcdrkrxbqb )
3. Kruse, R., Gebhardt, J., Klawonn, F.: Foundations of Fuzzy Systems. B.G. Teubner, Stuttgart, 1994.
4. Bruno, N.: Automated Physical Database Design and Tuning. CRC Press, Taylor & Francais Group, 2011.
5. Chao, Lee: Database Development and Management. Taylor & Francais Group, 2006.
6. Lecture notes on http://iat.fs.cvut.cz/dks/
List of problems for the exam
Database and Knowledge based systems
1. Data, data models, databases, Data Base Management System (DBMS).
2. Relational database systems - data types, relations (primary key, foreign key).
3. Entity relationship modeling.
4. Relational algebra and relational calculus.
5. Client - server systems.
6. Structured query language (SQL).
7. SQL: Select command - structure, examples. Aggregate functions. Use "select? for connecting tables.
8. SQL: Format and use of the "insert" and "update" commands.
9. SQL - DCL: Transaction, commit, rollback.

10. Database and Knowledge Based Systems - comparing.
11. Uncertainty in deciding.
12. Fuzzy logic.
13. Type of implications and inferences (Mamdani, Lukasiewicz, Larsen) and their properties.
14. Composition rule as a fuzzy inference.
15. Graphic construction for fuzzy inference for x=x0 according to Mamdani.
16. Rule based systems and computing in rule based systems.
17. Expert system (modular scheme, empty and dedicated expert system).
18. Knowledge base, representation of knowledge - types and kinds.
19. Inference engine and its cooperation with knowledge base.
20. Description of the expert systems: Expert system builder, ETS, Tracer.
21. Principles of Data Mining knowledge from Data bases.
22. Data Mining by Concept Lattices - essential concepts.
23. Method of Data Mining by Concept Lattices - procedure.
Database, SQL, DBMS, MS Access, Fuzzy sets, Fuzzy logic, Rule-based systems, Expert systems, Data mining, Hasse diagrem, Extraction of rules.
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