Scalable Uncertainty Management

Table of Contents

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   Book Overview
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   Chapter 1 Consistent Query Answering: The First Ten Years
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   Chapter 2 Heavy Tails and Web Models
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   Chapter 3 Managing Probabilistic Data with MystiQ: The Can-Do, the Could-Do, and the Can’t-Do
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   Chapter 4 Frequent Itemset Mining from Databases Including One Evidential Attribute
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   Chapter 5 Evaluating Trustworthiness from Past Performances: Interval-Based Approaches
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   Chapter 6 A Comparative Study of Six Formal Models of Causal Ascription
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   Chapter 7 An Efficient Algorithm for Naive Possibilistic Classifiers with Uncertain Inputs
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   Chapter 8 Transitive Observation-Based Causation, Saliency, and the Markov Condition
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    Chapter 9 A Family of Tolerant Antidivision Operators for Database Fuzzy Querying
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    Chapter 10 Uncertainty Management for the Retrieval of Economic Information from Distributed Markets
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    Chapter 11 Loopy Propagation in a Probabilistic Description Logic
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    Chapter 12 On the Performance of Fuzzy Data Querying
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    Chapter 13 Tractable Reasoning with Bayesian Description Logics
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    Chapter 14 Approximate Reasoning for Efficient Anytime Induction from Relational Knowledge Bases
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    Chapter 15 Fusing Uncertain Structured Spatial Information
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    Chapter 16 A Neuro Fuzzy Approach for Handling Structured Data
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    Chapter 17 A Framework for the Partial Evaluation of SPARQL Queries
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    Chapter 18 An Evolutionary Perspective on Approximate RDF Query Answering
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    Chapter 19 Clustering Uncertain Data Via K-Medoids
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    Chapter 20 Speeding Up the NRA Algorithm
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    Chapter 21 Uncertain Context Modeling of Dimensional Ontology Using Fuzzy Subset Theory
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    Chapter 22 A Personalized Approach to Experience-Aware Service Ranking and Selection
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    Chapter 23 Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results
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    Chapter 24 Optimization of Queries over Interval Probabilistic Data
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    Chapter 25 Polynomial Time Queries over Inconsistent Databases
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    Chapter 26 Using OBDDs for Efficient Query Evaluation on Probabilistic Databases
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    Chapter 27 A Logical Framework to Reinforcement Learning Using Hybrid Probabilistic Logic Programs
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    Chapter 28 On the Relationship between Hybrid Probabilistic Logic Programs and Stochastic Satisfiability
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    Chapter 29 Scaling Most Probable World Computations in Probabilistic Logic Programs
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    Chapter 30 Measuring the Ignorance and Degree of Satisfaction for Answering Queries in Imprecise Probabilistic Logic Programs