Overview

Military organizations around the world are normally huge producers and consumers of data. Accordingly, they stand to gain from the many benefits associated with data analytics. However, for leaders in defense organizations—either government or industry—accessible use cases are not always available. This book presents a diverse collection of cases that explore the realm of possibilities in military data analytics. These use cases explore such topics as: Context for maritime situation awareness Data analytics for electric power and energy applications Environmental data analytics in military operations Data analytics and training effectiveness evaluation Harnessing single board computers for military data analytics Analytics for military training in virtual reality environments A chapter on using single board computers explores their application in a variety of domains, including wireless sensor networks, unmanned vehicles, and cluster computing. The investigation into a process for extracting and codifying expert knowledge provides a practical and useful model for soldiers that can support diagnostics, decision making, analysis of alternatives, and myriad other analytical processes. Data analytics is seen as having a role in military learning, and a chapter in the book describes the ongoing work with the United States Army Research Laboratory to apply data analytics techniques to the design of courses, evaluation of individual and group performances, and the ability to tailor the learning experience to achieve optimal learning outcomes in a minimum amount of time. Another chapter discusses how virtual reality and analytics are transforming training of military personnel. Virtual reality and analytics are also transforming monitoring, decision making, readiness, and operations. Military Applications of Data Analytics brings together a collection of technical and application-oriented use cases. It enables decision makers and technologists to make connections between data analytics and such fields as virtual reality and cognitive science that are driving military organizations around the world forward.

Full Product Details

Author:   Kevin Huggins
Publisher:   Taylor & Francis Inc
Imprint:   Auerbach Publishers Inc.
Weight:   0.482kg
ISBN:  

9781498799768

ISBN 10:   1498799760
Pages:   194
Publication Date:   02 October 2018
Audience:   College/higher education ,  General/trade ,  Tertiary & Higher Education ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1 Bayesian Networks for Descriptive Analytics in Military Equipment Applications 2 Network Modeling and Analysis of Data and Relationships: Developing Cyber and Complexity Science 3 Context for Maritime Situation Awareness 4 Harnessing Single Board Computers for Military Data Analytics 5 Data Analytics and Training Effectiveness Evaluation 6 Data Analytics for Electric Power and Energy Applications 7 The Evolution of Environmental Data Analytics in Military Operations 8 Autoregressive Bayesian Networks for Information Validation and Amendment in Military Applications 9 Developing Cyber-Personas from Syslog Files for Insider Threat Detection: A Feasibility Study 10 Analytics for Military Training in Virtual Reality Environments

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Author Information

Kevin Huggins, PhD, is professor of Computer Science and Data Analytics at Harrisburg University of Science and Technology, Harrisburg, Pennsylvania. He is also a retired military officer who spent the early part of his career in military intelligence, with extensive experience in Latin America. The remainder of his career was spent in academia, primarily as a faculty member in the Department of Electrical Engineering and Computer Science at the U.S. Military Academy. While there, Dr. Huggins served as the director of Research in Network Science as well as the director of the Information Technology Program. Additionally, Dr. Huggins was a visiting scientist at the École de Techniques Avancées in Paris, France, where he studied parallel algorithms for multiprocessor system-on-chip (MPSoC) architectures. His current research interest lies at the intersection of data science and information security, exploring novel ways of securing computing systems by leveraging the enormous amounts of available data. He holds a PhD in computer science from Mines Paris Tech.

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