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 the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information…mehr
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 the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information systems require stronger capabilities in the area of uncertainty management. Our hope is that lasting interaction between these two areas would facilitate a new generation of information systems that will be capable of servicing these applications. Although there are researchers in information systems who have addressed themselves to issues of uncertainty, as well as researchers in uncertaintymodeling who have considered the pragmatic demands and constraints of information systems, to a large extent there has been only limited interaction between these two areas. As the subtitle, "From Needs to Solutions," indicates, this book presents view points of information systems experts on the needs that challenge the uncer tainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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