This book presents a contemporary view of the role of information quality in information fusion and decision making, and provides a formal foundation and the implementation strategies required for dealing with insufficient information quality in building fusion systems for decision making. Information fusion is the process of gathering, processing, and combining large amounts of information from multiple and diverse sources, including physical sensors to human intelligence reports and social media. That data and information may be unreliable, of low fidelity, insufficient resolution, contradictory, fake and/or redundant. Sources may provide unverified reports obtained from other sources resulting in correlations and biases. The success of the fusion processing depends on how well knowledge produced by the processing chain represents reality, which in turn depends on how adequate data are, how good and adequate are the models used, and how accurate, appropriate or applicable prior and contextual knowledge is. By offering contributions by leading experts, this book provides an unparalleled understanding of the problem of information quality in information fusion and decision-making for researchers and professionals in the field.

This book focuses on one of the major challenges of the newly created scientific domain known as data science: turning data into actionable knowledge in order to exploit increasing data volumes and deal with their inherent complexity. Actionable knowledge has been qualitatively and intensively studied in management, business, and the social sciences but in computer science and engineering, its connection has only recently been established to data mining and its evolution, ‘Knowledge Discovery and Data Mining’ (KDD). Data mining seeks to extract interesting patterns from data, but, until now, the patterns discovered from data have not always been ‘actionable’ for decision-makers in Socio-Technical Organizations (STO). With the evolution of the Internet and connectivity, STOs have evolved into Cyber-Physical and Social Systems (CPSS) that are known to describe our world today. In such complex and dynamic environments, the conventional KDD process is insufficient, and additional processes are required to transform complex data into actionable knowledge. Readers are presented with advanced knowledge concepts and the analytics and information fusion (AIF) processes aimed at delivering actionable knowledge. The authors provide an understanding of the concept of ‘relation’ and its exploitation, relational calculus, as well as the formalization of specific dimensions of knowledge that achieve a semantic growth along the AIF processes. This book serves as an important technical presentation of relational calculus and its application to processing chains in order to generate actionable knowledge. It is ideal for graduate students, researchers, or industry professionals interested in decision science and knowledge engineering.

The book emphasizes a contemporary view on the role of higher level fusion in designing crisis management systems, and provide the formal foundations, architecture and implementation strategies required for building dynamic current and future situational pictures, challenges of, and the state of the art computational approaches to designing such processes. This book integrates recent advances in decision theory with those in fusion methodology to define an end-to-end framework for decision support in crisis management. The text discusses modern fusion and decision support methods for dealing with heterogeneous and often unreliable, low fidelity, contradictory, and redundant data and information, as well as rare, unknown, unconventional or even unimaginable critical situations. Also the book examines the role of context in situation management, cognitive aspects of decision making and situation management, approaches to domain representation, visualization, as well as the role and exploitation of the social media. The editors include examples and case studies from the field of disaster management.

Managing information quality has become important in cyber-physical systems dealing with big data. In this regard, different models have been proposed, mainly in flat peer-to-peer networks, in which exchanging information efficiently is a key aspect due to scarce resources. However, little research has been conducted on information quality metrics for cyber-physical scenarios. In this paper, we propose an information quality metric and show its application to an information fusion model. It is a “model-oriented quality metric” since it allows non-predefined variants on its configuration depending on the application domain. The model was tested on several simulations using open datasets. The results obtained in the performance of the model confirm the validity of the information quality metric, proposed in this paper, on which the model is based. The model may have a wide variety of applications such as mobile recommendation or decision making in critical environments (emergencies, war, and so on).

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 defense and security.

Multisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world’s leading data fusion researchers and academicians, this authoritative book: Presents state-of-the-art advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors Describes new materials and achievements in optimal fusion and multisensor filters Discusses the advantages and challenges associated with multisensor data fusion, from extended spatial and temporal coverage to imperfection and diversity in sensor technologies Explores the topology, communication structure, computational resources, fusion level, goals, and optimization of multisensor data fusion system architectures Showcases applications of multisensor data fusion in fields such as medicine, transportation's traffic, defense, and navigation Multisensor Data Fusion: From Algorithms and Architectural Design to Applications is a robust collection of modern multisensor data fusion methodologies. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution.

This text reviews the fundamental theory and latest methods for including contextual information in fusion process design and implementation. Chapters are contributed by the foremost international experts, spanning numerous developments and applications. The book highlights high- and low-level information fusion problems, performance evaluation under highly demanding conditions, and design principles. A particular focus is placed on approaches that integrate research from different communities, emphasizing the benefit of combining different techniques to overcome the limitations of a single perspective. Features: introduces the terminology and core elements in information fusion and context; presents key themes for context-enhanced information fusion; discusses design issues in developing context-aware fusion systems; provides mathematical grounds for modeling the contextual influences in representative fusion problems; describes the fusion of hard and soft data; reviews a diverse range of applications.

"Proceedings of the NATO Advanced Study Institute on Multisensor Data and Information Processing for Rapid and Robust Situation and Threat Assessment, Albena, Bulgaria, 16-27 May 2005"--T.p. verso.

With the recent proliferation of service-oriented architectures (SOA), cloud computing technologies, and distributed-interconnected systems, distributed fusion is taking on a larger role in a variety of applications—from environmental monitoring and crisis management to intelligent buildings and defense. Drawing on the work of leading experts around the world, Distributed Data Fusion for Network-Centric Operations examines the state of the art of data fusion in a distributed sensing, communications, and computing environment. Get Insight into Designing and Implementing Data Fusion in a Distributed Network Addressing the entirety of information fusion, the contributors cover everything from signal and image processing, through estimation, to situation awareness. In particular, the work offers a timely look at the issues and solutions involving fusion within a distributed network enterprise. These include critical design problems, such as how to maintain a pedigree of agents or nodes that receive information, provide their contribution to the dataset, and pass to other network components. The book also tackles dynamic data sharing within a network-centric enterprise, distributed fusion effects on state estimation, graph-theoretic methods to optimize fusion performance, human engineering factors, and computer ontologies for higher levels of situation assessment. A comprehensive introduction to this emerging field and its challenges, the book explores how data fusion can be used within grid, distributed, and cloud computing architectures. Bringing together both theoretical and applied research perspectives, this is a valuable reference for fusion researchers and practitioners. It offers guidance and insight for those working on the complex issues of designing and implementing distributed, decentralized information fusion.

Scientists and engineers conducting research for military applicationsshare their findings on the semiautomation of the functionalities ofcognition, comprehension, and projection so that machines can replaceor enhance human awareness of a situation. A first volume surveysvarious options for practitioners, and this second volume identifiesoptions that have been chosen by the Technical Cooperation Programrepresentatives from different countries. It covers information fusionconcepts, distributed information fusion and management, human-systeminteraction, scenario-based design, and measures of effectiveness. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com).