The Internet of Things (IoT) and Big Data are hot topics in the world of intelligence operations and information-gathering. This first-of-its-kind volume reveals the benefits of addressing these topics with the integration of Fusion of Information and Analytics Technologies (FIAT). The book explains how FIAT is materialized into decision-support systems that are capable of supporting the prognosis, diagnosis, and prescriptive tasks within complex systems and organizations. This unique resource offers keen insight into how complex systems emerge from the interrelation of social and cognitive information, cyber and physical worlds, and the various models of decision-making and situational awareness. Practitioners also discover the central notions of analytics and information fusion. Moreover the book introduces propos such as integration through a FIAT computational model and applications at the systems level. This book concludes with a list of prospective research activities that can contribute towards the required FIAT integration for critical application domains such as: energy, health, transport and defence and security.

Machine generated contents note: 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 -- 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 -- 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 -- 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 (I) 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 -- 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 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 -- 6.1.Introduction -- 6.2.Interpretation of Information Elements (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 -- 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 -- 8.1.Introduction -- 8.2.Domain Knowledge Representation an d 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 -- References.