Information Fusion and Analytics for Big Data and IoT

Name: Information Fusion and Analytics for Big Data and IoT
Brand: Artech House
Price: 193.99 EUR
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Eloi Bosse(Autor*in)

Artech House (Verlag)

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Erschienen am 29. Februar 2016

280 Seiten

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Information Fusion and Analytics for Big Data and IoT
Contents
Preface
Chapter 1 General Context of Cyber Physical and Social Systems
1.1 Sociotechnical Organizations and CPSS
1.2 CPSS Versus Cyber Physical Systems
1.3 CPS Versus Internet of Things (IoT)
1.4 Information Overload and the Big Data Problem
1.5 Complexity and System Dependability
1.6 Fusion of Information and Analytics Technologies (FIAT)
1.7 Interface Between the Cyber and Physical Worlds: Four Cybernetic Functions
1.8 General Discussion on Decision Support Systems in Complex Dynamic CPSS
1.8.1 The Ideal Support System
1.8.2 Complex Situations
1.8.3 General Overarching System Engineering Concepts in CPSS
1.8.4 General Comments About Decision Support for a Smart Grid Environment
1.9 Summary
References
Chapter 2 Situation Awareness and Decision Support
2.1 Introduction
2.2 Decision-Making in Defense and Security CPSS
2.3 Decision-Making Models
2.3.1 OODA Loop Interdependencies: Situation Analysis and Decision-Making
2.3.2 Selection of the Best Decision-Making Models
2.4 Situations and Situation Analysis
2.4.1 Complex Events and Information Granules
2.4.2 Sense-Making
2.5 Situation Awareness
2.6 Cognitive System Engineering (CSE)
2.6.1 Cognitive Work Analysis (CWA) and Applied CWA
2.6.2 Ontological Engineering Versus CWA
2.7 Definition of Context
2.8 Definitions of Information Fusion and Analytics
2.8.1 Joint Directors of Laboratories (JDL) Data Fusion Model
2.8.2 Definition of Analytics
2.9 Situation Analysis Models (Information Fusion and Analytics)
2.9.1 OODA-Based Models
2.9.2 The Dual-Node Network (DNN) Architecture of the Revised JDL Model
2.9.3 The State Transition Data Fusion (STDF) Model
2.9.4 The Interpreted Systems Situation Analysis (ISSA) Approach
2.9.5 The Archetypal Dynamics Framework
2.10 Awareness Quality and Decision-Making: Information Quality and Quantity
2.11 Conclusion
References
Chapter 3 Information and Uncertainty: Definitions and Representations
3.1 Miscellaneous Views on Definition of Information
3.2 Semiotics
3.3 Information and Uncertainty
3.3.1 Information Theory and Entropy
3.3.2 Uncertainty and Risk
3.3.3 Dequech's Typology of Uncertainty in Economics
3.3.4 Dubois and Prade's Typology of Defects
3.3.5 Typologies of Uncertainty
3.4 Representations of Uncertainty
3.4.1 Quantitative Approaches
3.4.2 Qualitative Approaches
3.4.3 Hybrid and Graphical Approaches
3.5 General Uncertainty Principles in Data and Information Transformations
3.6 Measures of Uncertainty
3.6.1 Requirements for a Measure of Total Uncertainty
3.6.2 General Remarks on Measures of Uncertainty
3.7 Conclusion
References
Chapter 4 Information Characterization and Representations
4.1 Introduction
4.2 Definition of an Information Element
4.2.1 Objects, Contents, and Informative Relation
4.2.2 Internal/External Contexts
4.2.3 Illustrations of (J) at Signal and Data Levels of Abstraction
4.2.4 Illustrations of (J) at Higher Levels of Abstraction
4.2.5 Basic General Properties of an Information Element
4.3 Quality of Information (QoI)
4.3.1 The NATO SAS Command and Control (C2) Conceptual Reference Model
4.3.2 QoI Framework of Wang and Strong
4.3.3 On Defining Ontologies of QoI
4.4 Information Element Characterization: Intrinsic Dimensions
4.4.1 Information Inner Sets Scope
4.4.2 Informative Relation Arity
4.4.3 Informative Relation Objectivity
4.4.4 Information Imperfections
4.5 Contextual Information Characterization
4.5.1 Information Reliability
4.5.2 Information Consistency
4.5.3 Information Relevance
4.6 Belief Model Representation
4.6.1 Probability Theory
4.6.2 Dempster-Shafer Theory
4.6.3 Fuzzy Sets Theory
4.6.4 Possibility Theory
4.6.5 Fuzzy-Evidence Theory (Fuzzy-Belief Structures)
4.6.6 Summary on Information Element Representations
4.7 Conclusion
References
Chapter 5 Information Fusion Cells and Processing Strategies
5.1 Introduction
5.2 Defining an Information Fusion Cell
5.3 Processing Strategy 1: Information Fusion Cell, Type 1 (Data Fusion)
5.3.1 Building the Fused Information Element
5.3.2 Remarks on the Construction of the Joint Informative Relation
5.4 Processing Strategy 2: Information Fusion Cell, Type 2 (Parallel Belief Fusion)
5.5 Processing Strategy 3: Information Fusion Cell Type 3 (Sequential Belief Fusion)
5.5.1 Example 1: Fuzzy Relations Composition
5.5.2 Example 2: Belief Transformation
5.6 Processing Strategy 4: Information Fusion Cell Type 4 (Competitive Belief Fusion)
5.6.1 Dempster Parallel Basic Probability Assignment {AU: EDIT CORRECT?} Combination Rule
5.6.2 Hybrid Competitive Belief Models Fusion
5.7 Remarks on the Design of Distributed Information Fusion Systems (DIFS)
5.8 Conclusion
References
Chapter 6 A Holonic Processing of Information Fusion Cells
6.1 Introduction
6.2 Interpretation of Information Elements ({insert in-line equation here} as Holons and Informons
6.2.1 Holon Definition Within the Context of Information Fusion
6.3 Holon Characterization
6.3.1 Types of Holons According to Uncertainty Representations
6.4 Information Fusion Cell as a Network of Holons (H)
6.4.1 Holonic Information Fusion Core Functions
6.5 Quality and Performance of an Information Fusion Cell
6.5.1 Desired Information Fusion Cell Properties
6.6 Illustration of the Holonic Processing Framework: A Remote-Sensing Case
6.6.1 Context and Generation of Informons
6.6.2 Illustrative Example: A Remote-Sensing Case
6.6.3 JDL Level 1: Objects
6.6.4 JDL Level 2: Situations
6.6.5 JDL Level 3: Impact
6.7 Holonic Interpretation of the JDL Model
6.7.1 Complex Dynamic Systems
6.8 A Holonic Approach for Information Fusion
6.8.1 A Holonic View of the JDL Model
6.8.2 Examples of Holons and Informons in the JDL's Four Levels
6.8.3 Future Work on the Holonic JDL Model
6.9 Conclusion
References
Chapter 7 FIAT-Based Decision Support for Complex Environments
7.1 Introduction
7.2 System Interoperability in Heterogeneous Environments
7.2.1 Barès' Approach to Formalize Cooperability in Military Coalitions
7.2.1.4 Barès' Definition of an Interoperability Space
7.3 Integrated Frameworks to Process Imperfect Information
7.3.1 Integrated Frameworks to Process Imperfect Information
7.3.2 Sulis' Causal Tapestries
7.3.3 Abstract State Machines (ASM) to Engineer FIAT-Based Systems
7.3.3.3 Application Scenario: Tracking of Moving Objects
7.3.3.4 The Executable Abstract Model for the Scenario Rendezvous
7.4 Conclusion
References
Chapter 8 FIAT Prospective Research and Future Work
8.1 Introduction
8.2 Domain Knowledge Representation and Modeling
8.2.1 Ontology Management Methods in Power Network Domain Modeling
8.2.2 List of Research Activities for Domain Modeling in Power Networks
8.3 Processing of Information and Its Imperfections
8.4 Prognosis/Diagnosis/Prescriptive FIAT-Based DSS
8.5 Conclusion
References
Chapter 9 Summary
References
About the Authors
Index

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