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Table of Contents
Preface; Contents; Fundamentals; 1 Nearest Neighbor Queries on Big Data; Abstract; 1...Introduction; 2...Background; 3...System Model and Problem Formulation; 4...Related Work; 4.1 KNN Queries on Static Data; 4.2 Continuous KNN Queries; 4.3 All-KNN Queries in Distributed Systems; 4.4 Shortcomings of Existing Work; 5...The Proximity Framework; 5.1 Outline of Operation; 5.2 Constructing the Search Space; 5.3 Specialized Heap: The K+-Heap; 5.4 Insertion into the K+-Heap (Algorithm 2 and 3); 5.5 Running Example; 5.6 Performance Analysis; 6...Summary and Future Vision; References
2 Information Mining for Big InformationAbstract; 1...Introduction; 2...Information Mining in Knowledge Discovery from Data; 3...Strong Relevant Logic-Based Reasoning as an Information Mining Method; 3.1 Deduction, Induction, and Abduction in Information Mining; 3.2 Formal Logic System and Formal Theory; 3.3 Reasoning with Strong Relevant Logics; 4...Supporting Tools for Information Mining with Strong Relevant Logic-Based Reasoning; 4.1 Forward Reasoning Engine; 4.2 Truth Maintenance System; 4.3 Epistemic Programming; 5...Summary; References
3 Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression AnalysisAbstract; 1...Introduction; 2...Some Related Studies; 2.1 Recall of Real-Time Data Analysis Processing; 2.2 Brief Review on Granular Information; 2.3 Genetically-Guided Clustering Algorithm; 2.4 A Brief Review of a Convex Hull Approach; 2.4.1 Affine, Convex Hull Definition and Supporting Hyperplane; 2.4.2 Beneath-Beyond Algorithm; 2.5 A Convex Hull-Based Regression; 3...A Real-Time Granular Based Fuzzy Regression Models with a Convex Hull Implementation; 4...A Numerical Example and Performance Analysis
5...Conclusion and Future WorksAcknowledgments; References; 4 How to Understand Connections Based on Big Data: From Cliques to Flexible Granules; Abstract; 1...Understanding Connections Based on Big Data: An Important Practical Problem; 2...General Case: How to Describe Available Information; 3...A Known Semi-heuristic Method for Detecting True Connections Based on Big Data: A Brief Description; 4...Limitations of the Semi-heuristic Approach; 5...Analysis of the Problem and the Resulting Ideas and Formulas; 6...Towards an Algorithm; 7...Resulting Algorithm; 8...Conclusions; Acknowledgements; References
5 Graph-Based Framework for Evaluating the Feasibility of Transition to MaintainomicsAbstract; 1...Introduction; 2...Background; 2.1 What is Maintenance?; 2.2 What is e-Maintenance?; 2.3 What is Big Data?; 2.4 What is Maintainomics?; 3...Problem Statement; 4...Proposed Methodology; 4.1 Background of Graph Theory; 4.2 Background of Feasibility Index of Transition; 4.2.1 Visualizing Enablers' Correlation via Digraph; 4.2.2 Interpreting Enablers' Digraph Through Matrix; 4.2.3 Establishing the Matrix's Permanent Function Expression; 4.2.4 Creating FIT Value Scale; 4.2.5 Comparison; 4.2.6 Summary
2 Information Mining for Big InformationAbstract; 1...Introduction; 2...Information Mining in Knowledge Discovery from Data; 3...Strong Relevant Logic-Based Reasoning as an Information Mining Method; 3.1 Deduction, Induction, and Abduction in Information Mining; 3.2 Formal Logic System and Formal Theory; 3.3 Reasoning with Strong Relevant Logics; 4...Supporting Tools for Information Mining with Strong Relevant Logic-Based Reasoning; 4.1 Forward Reasoning Engine; 4.2 Truth Maintenance System; 4.3 Epistemic Programming; 5...Summary; References
3 Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression AnalysisAbstract; 1...Introduction; 2...Some Related Studies; 2.1 Recall of Real-Time Data Analysis Processing; 2.2 Brief Review on Granular Information; 2.3 Genetically-Guided Clustering Algorithm; 2.4 A Brief Review of a Convex Hull Approach; 2.4.1 Affine, Convex Hull Definition and Supporting Hyperplane; 2.4.2 Beneath-Beyond Algorithm; 2.5 A Convex Hull-Based Regression; 3...A Real-Time Granular Based Fuzzy Regression Models with a Convex Hull Implementation; 4...A Numerical Example and Performance Analysis
5...Conclusion and Future WorksAcknowledgments; References; 4 How to Understand Connections Based on Big Data: From Cliques to Flexible Granules; Abstract; 1...Understanding Connections Based on Big Data: An Important Practical Problem; 2...General Case: How to Describe Available Information; 3...A Known Semi-heuristic Method for Detecting True Connections Based on Big Data: A Brief Description; 4...Limitations of the Semi-heuristic Approach; 5...Analysis of the Problem and the Resulting Ideas and Formulas; 6...Towards an Algorithm; 7...Resulting Algorithm; 8...Conclusions; Acknowledgements; References
5 Graph-Based Framework for Evaluating the Feasibility of Transition to MaintainomicsAbstract; 1...Introduction; 2...Background; 2.1 What is Maintenance?; 2.2 What is e-Maintenance?; 2.3 What is Big Data?; 2.4 What is Maintainomics?; 3...Problem Statement; 4...Proposed Methodology; 4.1 Background of Graph Theory; 4.2 Background of Feasibility Index of Transition; 4.2.1 Visualizing Enablers' Correlation via Digraph; 4.2.2 Interpreting Enablers' Digraph Through Matrix; 4.2.3 Establishing the Matrix's Permanent Function Expression; 4.2.4 Creating FIT Value Scale; 4.2.5 Comparison; 4.2.6 Summary