Military Applications of Data Analytics


Book Description

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.




Leveraging Big Data Analytics to Improve Military Recruiting


Book Description

The authors identified current, desired, and prospective data-enabled practices that the U.S. Department of Defense and the services might be able to deploy in their outreach and recruiting processes.




Assessing Department of Defense Use of Data Analytics and Enabling Data Management to Improve Acquisition Outcomes


Book Description

"In the conference report accompanying the National Defense Authorization Act for Fiscal Year 2017, Congress expressed concern that the U.S. Department of Defense (DoD) "does not sufficiently incorporate data into its acquisition-related learning and decision-making" and asked six questions about "the use of data analysis, measurement, and other evaluation-related methods in DoD acquisition programs." In this report, the authors decompose and measure acquisition functions, data governance, and training to assess how data and associated analytics support DoD acquisition decision making. The authors found that the DoD is applying a breadth of data analytics to acquisition. Capabilities range from simple data archives and plotting to archives integrated with commercial analytic tools. The DoD has implemented an array of data governance and management practices, but major challenges remain, including a culture against data sharing and concerns about security and oversight burden. Some commercial breakthroughs in advanced analytics sound promising for DoD acquisition, but some might not be applicable; research is ongoing. Advancement should include developing a data analytics strategy across acquisition domains, expanding data governance and data sharing, and continuing to expand and mature data collection, access, and analytic layers. Also, mechanisms are needed to authorize and ensure protected access to data for both the DoD and external analysts. Improved incentives and understanding of data analytics could encourage decision makers to make better use of capabilities" -- Publisher's description.




Cognitive Social Mining Applications in Data Analytics and Forensics


Book Description

Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.




Data Analytics and AI


Book Description

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.




Data Analytics


Book Description

Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques. Data analytics are becoming an essential component for every organization and technologies such as health care, financial trading, Internet of Things, Smart Cities or Cyber Physical Systems. However, these diverse application domains give rise to new research challenges. In this context, the book provides a broad picture on the concepts, techniques, applications, and open research directions in this area. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.




Meeting Security Challenges Through Data Analytics and Decision Support


Book Description

The sheer quantity of widely diverse data which now results from multiple sources presents a problem for decision-makers and analysts, who are finding it impossible to cope with the ever-increasing flow of material. This has potentially serious consequences for the quality of decisions and operational processes in areas such as counterterrorism and security. This book presents the papers delivered at the NATO Advanced Research Workshop (ARW) 'Meeting Security Challenges through Data Analytics and Decision Support’, held in Aghveran, Armenia, in June 2015. The aim of the conference was to promote and enhance cooperation and dialogue between NATO and Partner countries on the subject of effective decision support for security applications. The attendance of many leading scientists from a variety of backgrounds and disciplines provided the opportunity to improve mutual understanding, as well as cognizance of the specific requirements and issues of Cyber Physical Social Systems (CPPS) and the technical advances pertinent to all collaborative human-centric information support systems in a variety of applications. The book is divided into 3 sections: counter terrorism: methodology and applications; maritime and border security; and cyber security, and will be of interest to all those involved in decision-making processes based on the analysis of big data.




Data Science Concepts and Techniques with Applications


Book Description

This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.




Pivoting Government through Digital Transformation


Book Description

Affecting every sector and country in the world, digital technology is changing the way citizens engage in society, companies conduct business, and governments deliver public services. The COVID-19 pandemic accelerated the pace of digitalization and exposed such vulnerabilities as inadequate infrastructure, weak regulations, and a scarcity of skilled professionals capable of digitally transforming government. Not immune to the digital revolution, governments are slowly adapting to a digital world. Governments are implementing digital solutions to deliver services to their citizens, make payments, and engage the public. Focusing on how government can transition more effectively through digital transformation, Pivoting Government Through Digital Transformation covers the following key components: Setting the stage during the Great Resignation period Filling the digital talent pipeline Best practices and vignettes for applying digital transformation in government Looking ahead towards the future Key chapter contributors from U.S. and foreign governments, as well as state and local governments, discuss how they are coping with today’s environment and how they are using digital transformation efforts to enhance their organization’s effectiveness and digital talent pipeline. With chapters on theory and practice, this groundbreaking book offers an in-depth analysis of the most innovative approaches to e-government and discusses case studies from local, state, and federal government perspectives. This is an essential guide for government employees, scholars, and regular citizens who want to make government work more effectively and democratically in the digital age.




Intelligent Data Analytics for Terror Threat Prediction


Book Description

Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. This book provides innovative insights that will help obtain interventions to undertake emerging dynamic scenarios of criminal activities. Furthermore, it presents emerging issues, challenges and management strategies in public safety and crime control development across various domains. The book will play a vital role in improvising human life to a great extent. Researchers and practitioners working in the fields of data mining, machine learning and artificial intelligence will greatly benefit from this book, which will be a good addition to the state-of-the-art approaches collected for intelligent data analytics. It will also be very beneficial for those who are new to the field and need to quickly become acquainted with the best performing methods. With this book they will be able to compare different approaches and carry forward their research in the most important areas of this field, which has a direct impact on the betterment of human life by maintaining the security of our society. No other book is currently on the market which provides such a good collection of state-of-the-art methods for intelligent data analytics-based models for terror threat prediction, as intelligent data analytics is a newly emerging field and research in data mining and machine learning is still in the early stage of development.