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Uncertainty Management in Information Systems From Needs to Solutions

As its title suggests, "Uncertainty Management in Information Systems" is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on th...

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Bibliographic Details
Other Authors: Motro, Amihai (Editor), Smets, Philippe (Editor)
Format: eBook
Language:English
Published: New York, NY Springer US 1997, 1997
Edition:1st ed. 1997
Subjects:
Operations Research
Operations Research/decision Theory
Artificial Intelligence
Decision Making
Natural Language Processing (nlp)
Natural Language Processing (computer Science)
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 9 Probabilistic and Bayesian Representations of Uncertainty in Information Systems: a Pragmatic Introduction
  • 1 Introduction and Overview
  • 2 Basic Issues in Bayesian Probability
  • 3 Probabilistic Representations of Alternative Types of Uncertainty
  • 4 Example Problems and Their Bayesian Solutions
  • 5 Representing Uncertainty in Large Databases
  • 6 Conclusions
  • 10 An Introduction to the Fuzzy Set and Possibility Theory-Based Treatment of Flexible Queries and Uncertain or Imprecise Databases
  • 1 Introduction
  • 2 Imperfect Information: Vocabulary
  • 3 Fuzzy Databases
  • 4 Flexible Queries
  • 5 Imperfect Data in a Database
  • 6 Integrity Constraints and Fuzzy Functional Dependencies
  • 7 Concluding Remarks
  • 11 Logical Handling of Inconsistent and Default Information
  • 1 Introduction
  • 2 Handling Inconsistent Information
  • 3 Handling Default Information
  • 4 Labeled Deductive Systems for Practical Reasoning
  • 5 Conclusions
  • 12 The Transferable Belief Model for Belief Representation
  • 1 Introduction
  • 2 The Transferable Belief Model
  • 3 The Mathematics of the TBM
  • 4 Applications to Databases
  • 5 Application with Sources Reliability
  • 6 Application for Diagnosis
  • 7 Conclusions
  • 13 Approximate Reasoning Systems: Handling Uncertainty and Imprecision in Information Systems
  • 1 Introduction
  • 2 Probabilistic Approaches
  • 3 Fuzzy Logic Based Approaches
  • 4 Conclusions
  • 14 On the Classification of Uncertainty Techniques in Relation to the Application Needs
  • 1 Introduction
  • 2 On the Classification of Uncertainty Techniques
  • 3 On Sources of Uncertainty
  • 4 Building Applications with Uncertainty Management
  • 5 Conclusions
  • 15 A Bibliography on Uncertainty Management in Information Systems
  • 1 Introduction
  • 2 Surveys
  • 3 Null Values
  • 4 Logic
  • 5 Fuzzy Set and Possibility Theory
  • 6 Probability Theory
  • 7 Query-level Uncertainty
  • 8 Schema-level Uncertainty
  • 4 Database Error Controls
  • 5 Data Accuracy and Database Performance
  • 6 Missing Categorical Data
  • 7 Conclusions
  • 6 Knowledge Discovery and Acquisition from Imperfect Information
  • 1 Introduction
  • 2 Uncertainty Management
  • 3 Knowledge Discovery in Databases
  • 4 Knowledge Acquisition
  • 5 Sources of Imperfection in Discovered Patterns
  • 6 Summary
  • 7 Uncertainty In Information Retrieval Systems
  • 1 Introduction
  • 2 Background
  • 3 Principal Retrieval Models
  • 4 Current Trends in Information Retrieval
  • 5 Open Problems
  • 8 Imperfect Information: Imprecision and Uncertainty
  • 1 Imperfect Information
  • 2 Varieties of Imperfect Information
  • 3 Modeling
  • 4 Combining Models of Ignorance
  • 5 Conclusion
  • Appendix A: A Structured Thesaurus of Imperfection
  • Appendix B: Thesaurus on Uncertainty and Incompleteness
  • Appendix C: Models for Uncertainty on Finite Frames
  • 1 Introduction
  • 1 Scope
  • 2 Structure
  • 2 Sources of Uncertainty, Imprecision, and Inconsistency in Information Systems
  • 1 Introduction
  • 2 Imperfect Descriptions: Classification
  • 3 Imperfect Descriptions: Solutions
  • 4 Imperfect Manipulation and Processing
  • 5 Challenges
  • 3 Imperfect Information in Relational Databases
  • 1 Introduction
  • 2 Possible Worlds
  • 3 Manipulating an Imperfect Database
  • 4 Existential Values
  • 5 Inexistent Values
  • 6 Open Databases and Null Values
  • 7 Combination of Null Values
  • 8 Universal Relation Databases and Null Values
  • 9 Null Values in Nested Relational Databases
  • 10 Maybe Tuples
  • 11 Disjunctive Databases
  • 12 Probabilistic Databases
  • 4 Uncertainty in Intelligent Databases
  • 1 Introduction
  • 2 Incompleteness
  • 3 Validity
  • 4 Conclusion
  • 5 Uncertain, Incomplete, and Inconsistent Data in Scientific and Statistical Databases
  • 1 Introduction
  • 2 Sources of Uncertainty
  • 3 Example Databases
  • 9 Complexity Analyses
  • 10 Miscellaneous

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