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. - 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 Uncertaintyon Finite Frames. - 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 andPossibility Theory. - 6 Probability Theory. - 7 Query-level Uncertainty. - 8 Schema-level Uncertainty. - 9 Complexity Analyses. - 10 Miscellaneous. Language: English