Database and Knowledge Systems

Lecturer:

František Král, Jiří Bíla (gar.), Ivo Bukovský

Tutor:

František Král, Jiří Bíla (gar.), Ivo Bukovský, Vladimír Hlaváč

Synopsis:

Basic data models. Types and examples of database systems. Management of database systems. Design of database systems - examples. Programming techniques. Language SQL. SQL for ORACLE. Fundamentals of programming in database systems MS ACCESS and ORACLE.

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. Principles of Data-mining in databases. Expert systems - modular structure. Examples of expert systems: ETS, NEST, CLIPS, FEL-EXPERT, TRACER.

Requirements:

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 the system of ORACLE-type..

P8. Data-based and Knowledge-Based systems. Operations with knowledge. Decision and

Control paradigms. Operations with uncertainties. Theory of fuzzy sets.

P9. Operations with fuzzy sets. Fuzzy numbers and computing with fuzzy numbers.

Linguistic variable.

P10. Fuzzy logic. Compositional rule and fuzzy inference. Types of fuzzy implications and

their properties.

P11. Rule-based systems. (Examples.)

P12. Types and kinds of knowledge representation. Expert system - modular structure.

Inference engine and its co-operations with knowledge-base. System ETS.

P13. Expert systems - examples (FELExpert, TRACER, XCON/XSELL, KEE, NEST,

G2).

P14. Future of database and knowledge systems. Conclusions of lectures.

Syllabus of tutorials:

C1. Fundamentals of work with Data-base systems.

C2. Data-based systems for non-programmers.

C3. Introduction in data-base system MS ACCESS. Realisation of set and relational

operations.

C4. Structured language - SQL. Assignment of semester tasks.

C5. Programming in MS ACCESS.

C6. Programming in data-base system ORACLE.

C7. Programming in ACCESS versus programming in ORACLE.

C8. Fuzzy numbers, computing with fuzzy numbers.

C9. Fuzzy logic. Compositional rule and fuzzy inference. Types of fuzzy implications and

their properties.

C10. Rule-based systems. Expert system ETS. Assignment of semester tasks.

C11. Expert system ETS - examples. Expert system NEST.

C12. Expert system NEST.

C13. Assistance and testing of results of semester tasks.

C14. 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 the system of ORACLE-type.., P8. Data-based and Knowledge-Based systems. Operations with knowledge. Decision and, Control paradigms. Operations with uncertainties. Theory of fuzzy sets., P9. Operations with fuzzy sets. Fuzzy numbers and computing with fuzzy numbers., Linguistic variable., P10. Fuzzy logic. Compositional rule and fuzzy inference. Types of fuzzy implications and, their properties., P11. Rule-based systems. (Examples.), P12. Types and kinds of knowledge representation. Expert system - modular structure., Inference engine and its co-operations with knowledge-base. System ETS., P13. Expert systems - examples (FELExpert, TRACER, XCON/XSELL, KEE, NEST,, G2)., P14. Future of database and knowledge systems. Conclusions of lectures.

Study materials:

Bíla, Král : Databázové a znalostní systémy. ČVUT, Praha, 1999., Vysoký, P.: Fuzzy řízení. ČVUT, Praha, 1999., Mařík, V. a kol.: Umělá inteligence, díl 2., kap. 1., 2., 3. a 9. ACADEMIA, Praha, 1997

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans: