Advanced Database Models

Enrollement in the course requires an successful completion of the following courses:

Implementation of Semantic Web (PI-ISW)

Lecturer:

Peter Vojtáš (gar.)

Tutor:

Supervisor:

Department of Software Engineering

Synopsis:

In the time when we are flooded with information it is important to order query answer by user preferences. In this lecture we present models of representation, use and mining of preferences. More attention will be devoted to monotone Datalog with preferences (correctness, completeness, fixed point). We will show connections to models of R. Fagin for top-k querying in the area of web service integration (correctness and optimality of Fagin's TA and NRA algorithms). We will deal also with measures for evaluation of success of preference querying.

Requirements:

Completition of subject PI-ISW Implementation of the Semantic Web.

Syllabus of lectures:

1.Introduction, motivation, problems and use cases of user preferences

2.User models, representation of preferences

3.Querying, search and reasoning with preferences

4.The monotone model and optimality of Fagin's algorithms for top-k querying

5.Fuzzy logic as a language for modeling of preferences, many valued modus pones

6.Procedural and declarative semantics of fuzzy Datalog without negation, correctness of fuzzy Datalog

7.Fixpoint for fuzzy Datalog and computability of the minimal model

8.Theorem on approximate completeness of fuzzy Datalogu

9.Equivalence with generalized annotated programs, fuzzy similarity

10.Various models of user and his/her interaction (both direct and indirect), temporal aspects of procedural models of software systems with user

11.Formulation of the problem of learning (acquisition) of user preference

12.Various models of evaluation of quality of models of user preferences

13.Connections to economical and optimization models

Syllabus of tutorials:

Excercises have the form of individual consultations.

Study Objective:

In the time when we are flooded with information it is important to order query answer by user preferences. In this lecture we present models of representation, use and mining of preferences. More attention will be devoted to monotone Datalog with preferences (correctness, completeness, fixed point). We will show connections to models of R. Fagin for top-k querying in the area of web service integration (correctness and optimality of Fagin's TA and NRA algorithms). We will deal also with measures for evaluation of success of preference querying.

Study materials:

1.A comparison of fuzzy and annotated logic programming, Fuzzy Sets and Systems, 144 (2004) 173-192

2.A data model for flexible querying. In Proc. ADBIS'01, Lecture Notes in Computer Science 2151, Springer Verlag, Berlin 2001, 280-293

3.Fagin, Lotem, Naor. Optimal aggregation algorithms for middleware, J. Computer and System Sciences 66 (2003), pp. 614-656

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:

• Informatics (VO)