Module Import 04IN2022 - Advanced Data Modeling

Status: (discontinued) Published
Workload6 ECTS = 180 hrs
Credits, Weight6 ECTS, (n.s.)
Language of Instruction English
Semester (n.s.)
Duration1 Sem.
M/E Elective
Courses
Course No. Type Name MA/EL Workload Credits Contact Hours Selfstudy Group Size
04IN2022-1 Lecture Advanced Data Modeling (n.s.) 3 ECTS = 90 hrs - 2 hrs/week = 30 hrs 60 hrs (n.s.)
04IN2022-2 Exercise Advanced Data Modeling (n.s.) 3 ECTS = 90 hrs - 2 hrs/week = 30 hrs 60 hrs (n.s.)
Learning Outcomes

Students understand the logical foundations of database systems. They can apply logical foundations to new data models (XML, RDF, graph data formats) designing semantically complete and correct models and can derive implementations. They understand the advantages and disadvantages of different logics-based data modelling paradigms and are able to integrate corresponding systems into software.

Content

(not specified)

04IN2022-1 - Advanced Data Modeling
  1. Foundations
    • Repetition: Relational model
    • Repetition: First order logics
  2. Minimal model semantics
    • Minimal models
    • Definite programmes
    • Stratification
    • Procedural semantics for minimal models
    • Well-founded semantics
    • Many valued models
  3. Answer set programming
    • Stable models
    • Models and techniques in answer set programming
    • Implementation techniques
    • The DLV System
Teaching Methods

(not specified)

Prerequisites

Basic knowledge about first order logics.

Examination Methods

Oral or written exam depending on class size.

Participation in the tutorial is a prerequisite for admission to the examination.

Stellenwert für die Note in der Endnote: Für Lehramt Gymnasium: 5% entsprechend den LP (6:120) Für Lehramt Realschule: 10% entsprechend den LP (6:60)

Credit Requirements

(not specified)

References

(not specified)

04IN2022-1 - Advanced Data Modeling

Lloyd: Foundations of Logic Programming

François Bry, Norbert Eisinger, Thomas Eiter, Tim Furche, Georg Gottlob, Clemens Ley, Benedikt Linse, Reinhard Pichler, Fang Wei: Foundations of Rule-Based Query Answering. Reasoning Web 2007: 1-153. Springer Verlag, 2007

Melvin Fitting: Fixpoint semantics for logic programming a survey. Theor. Comput. Sci. 278(1-2): 25-51 (2002).

Thomas Eiter, Giovambattista Ianni, and Thomas Krennwallner. Answer Set Programming: A Primer. In: Reasoning Web. Semantic Technologies for Information Systems, 5th International Summer School 2009, Brixen-Bressanone, Italy, August 30 - September 4, 2009, Tutorial Lectures. Lecture Notes in Computer Science 5689 Springer 2009, pp. 40-110.

Nicola Leone, Pasquale Rullo, Francesco Scarcello. Disjunctive Stable Models: Unfounded Sets, Fixpoint Semantics, and Computation. In: Information and Computation, 135(2): 69-112.

Thomas Eiter, Nicola Leone, Cristinel Mateis, Gerald Pfeifer, Franceseo Scarcello. A Deductive System for Non-Monotonic Reasoning. In: Logic Programming And Nonmonotonic Reasoning. Lecture Notes in Computer Science, 1997, Volume 1265/1997, 363-374, Springer.

Gerhard Brewka, Thomas Eiter, Miroslaw Truszczynski: Answer set programming at a glance. Commun. ACM 54

Use of this Module
  1. unmodified as Elective  -    BSc Computer Science 2017  -    Mandatory elective courses Computer Science  -    Advanced Data Modeling
  2. unmodified as Elective  -    BSc Computational Visualistics 2017  -    Mandatory elective courses Computer Science  -    Advanced Data Modeling
  3. unmodified as Elective  -    BSc Computational Visualistics 2017  -    Mandatory elective courses in Computational Visualistics or computer science  -    Advanced Data Modeling
  4. unmodified as Elective  -    MSc Computer Science 2017  -    Mandatory elective courses Computer Science  -    Advanced Data Modeling
  5. unmodified as Elective  -    MSc Computer Science 2017  -    Major subject computer science  -    Data and Knowledge Engineering  -    Advanced Data Modeling
  6. unmodified as Elective  -    MSc Computational Visualistics 2017  -    Mandatory elective courses Computer Science  -    Advanced Data Modeling
  7. unmodified as Elective  -    MSc Computational Visualistics 2017  -    Mandatory elective courses in Computational Visualistics or computer science  -    Advanced Data Modeling
  8. unmodified as Elective  -    MSc Information Management 2017  -    Mandatory elective courses Computer Science and Information Systems  -    Advanced Data Modeling
  9. unmodified as Elective  -    MSc Information Systems 2017  -    Mandatory elective courses Application Systems in Business and Administration  -    Advanced Data Modeling
  10. unmodified as Elective  -    MSc Web Science 2017  -    Mandatory elective courses Computer Science  -    Advanced Data Modeling
Responsible / Organizational Unit
Staab, Steffen / Institute for Computer Science
Additional Information

(not specified)

Last change
Apr 24, 2018 by Frey, Johannes
Last Change Module
Jun 11, 2013 by Frey, Johannes