As of August 2024, International Journal "Notes on Intuitionistic Fuzzy Sets" is being indexed in Scopus.
Please check our Instructions to Authors and send your manuscripts to nifs.journal@gmail.com. Next issue: September/October 2024.

Open Call for Papers: International Workshop on Intuitionistic Fuzzy Sets • 13 December 2024 • Banska Bystrica, Slovakia/ online (hybrid mode).
Deadline for submissions: 16 November 2024.

Generalized Nets in Artificial Intelligence. Volume 4: Generalized Nets, Uncertain Data and Knowledge Engineering

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Revision as of 20:42, 20 July 2009 by Vassia Atanassova (talk | contribs) (cat)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search
Cover of the book

Generalized Nets in Artificial Intelligence. Volume 4: Generalized Nets, Uncertain Data and Knowledge Engineering is a book by Panagiotis Chountas, Boyan Kolev, Ermir Rogova, Violeta Tasseva and Krassimir Atanassov, published in 2007 by "Prof. Marin Drinov" Publishing house of the Bulgarian Academy of Sciences, under ISBN 978-954-322-255-1.

Table of contents

  • Chapter 1: Introduction
    • 1.1. Treatment of Uncertainty in Non-Static Data Repositories
    • 1.2. Data Uncertainty in Perception
    • 1.3. Uncertainty in Databases
    • 1.4. Definition of Ignorant & Uncertainty Information
    • 1.5. Ignorance in OLAP & Data-Warehouses
    • 1.6. Conclusions
  • Chapter 2: Generalized Nets and Intuitionistic Fuzzy OLAP
    • 2.1. Introduction
    • 2.2. Semantics of the IF Cube in Contrast to Crisp Cube
    • 2.3. Basic Operators
    • 2.4. Extended Operators
    • 2.5. Group Operations & Operators
    • 2.6. The Case for Knowledge Based OLAP-KNOLAP
    • 2.7. GN Model of Selection Operation over IF OLAP Cube
    • 2.8. GN Model for Querying Operators over Multiple IF OLAP Cubes
    • 2.9. GN Model of Binary Operations over IF OLAP Cubes
    • 2.10. Conclusions
  • Chapter 3: Generalized Net Representation of Data Medication
    • 3.1. Introduction
    • 3.2. Data Integration Approaches
    • 3.3. Principles of Data Integration
    • 3.4. Partial Values & Background Knowledge in Mediation
    • 3.5. Mediation and Constraints
    • 3.6. Functioning of the Intuitionistic Fuzzy Mediator
    • 3.7. Querying Probabilistic and Null Values in a Common Way
    • 3.8. Intuitionistic Fuzzy Depiction of Probabilistic Data
      • 3.8.1. Performing Selection Operation Using IFRDBMS
      • 3.8.2. Performing join using IFRDBMS
    • 3.9. Intuitionistic Fuzzy Depiction of Null Values
    • 3.10. Data Storage Requirements
    • 3.11. Conclusions
  • Chapter 4: Generalized Nets and Evolving Intuitionistic Fuzzy Data Warehouses
    • 4.1. Introduction
    • 4.2. The Concept of Time
    • 4.3. Time Concepts and Types of Temporal Information
    • 4.4. Time and Types of Data
    • 4.5. Conceptual Model for Versioning Data Warehouses
    • 4.6. Query Model for Versioning Data Warehouses
    • 4.7. Alternative Approaches for Non Versioning Data Warehouses
    • 4.8. Query Model for XML Data Warehouses
    • 4.9. GN-model of Unary and Binary Read-Only Operations in a Data Warehouse
    • 4.10. GN-model of Common Read-Only Operations in a Data Warehouse
    • 4.11. GN-model of Unary and Binary Read/Write Operations in a Versioned Data Warehouse
    • 4.12. GN-model of Common Read/Write Operations in a Versioned Data Warehouse
    • 4.13. Conclusions
  • Chapter 5: Conclusions
  • Appendix A: Intuitionistic Fuzzy Sets and H-IFSs
    • Basic definitions
    • IFS Over Hierarchical Universes
  • Appendix B: Generalized Nets
    • Basic definitions
  • References

See also