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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Database and Knowledge-based Systems

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Code Completion Credits Range Language
E371079 Z,ZK 5 3P+1C English
Lecturer:
Jiří Bíla (guarantor), Vladimír Hlaváč
Tutor:
Jiří Bíla (guarantor), Vladimír Hlaváč
Supervisor:
Department of Instrumentation and Control Engineering
Synopsis:

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.

Requirements:

List of problems for the exam

Database and Knowledge based systems

2016/2017

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.

Syllabus of lectures:

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.

Syllabus of tutorials:

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.

Study Objective:

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 - Expert System Builder, System ETS, NEST, 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. Detection of unexpected situations. Conclusions of lectures.

Study materials:

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/

Note:
Time-table for winter semester 2019/2020:
Time-table is not available yet
Time-table for summer semester 2019/2020:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Fri
Thu
roomT4:C1-308
Bíla J.
Hlaváč V.

13:15–15:00
(lecture parallel1)
Dejvice
Laboratoř 12110.3 - 308
roomT4:C1-308
Bíla J.
Hlaváč V.

15:00–16:45
(lecture parallel1
parallel nr.1)

Dejvice
Laboratoř 12110.3 - 308
Fri
The course is a part of the following study plans:
Data valid to 2020-08-15
For updated information see http://bilakniha.cvut.cz/en/predmet1642906.html