This text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it exists and illustrates the program code and output. The data appendix provides many real data sets-beyond those used for the examples-which can serve as the basis for exercises.

DATA VISUALIZATION: Exploring and Explaining with Data is designed to introduce best practices in data visualization to undergraduate and graduate students. This is one of the first books on data visualization designed for college courses. The book contains material on effective design, choice of chart type, effective use of color, how to both explore data visually, and how to explain concepts and results visually in a compelling way with data. The book explains both the "why" of data visualization and the "how." That is, the book provides lucid explanations of the guiding principles of data visualization through the use of interesting examples.

With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market. Comparing and contrasting the different types of analysis commonly conducted with big data, this accessible reference presents clear-cut explanations of the general workings of big data tools. Instead of spending time on HOW to install specific packages, it focuses on the reasons WHY readers would install a given package. The book provides authoritative guidance on a range of tools, including open source and proprietary systems. It details the strengths and weaknesses of incorporating big data analysis into decision-making and explains how to leverage the strengths while mitigating the weaknesses. Describes the benefits of distributed computing in simple terms Includes substantial vendor/tool material, especially for open source decisions Covers prominent software packages, including Hadoop and Oracle Endeca Examines GIS and machine learning applications Considers privacy and surveillance issues The book further explores basic statistical concepts that, when misapplied, can be the source of errors. Time and again, big data is treated as an oracle that discovers results nobody would have imagined. While big data can serve this valuable function, all too often these results are incorrect, yet are still reported unquestioningly. The probability of having erroneous results increases as a larger number of variables are compared unless preventative measures are taken. The approach taken by the authors is to explain these concepts so managers can ask better questions of their analysts and vendors as to the appropriateness of the methods used to arrive at a conclusion. Because the world of science and medicine has been grappling with similar issues in the publication of studies, the authors draw on their efforts and apply them to big data.

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.

- the book provides a unique overview of the NCBI resources, including BLAST (which are foundational to bioinformatics), and how to use them, making it a great introduction to bioinformatics and a great resource for those just starting in an industry lab - whereas many bioinformatics books try to cover every aspect of the topic and easily confuse readers, this is highly practical and focuses on key resources and tools, and how to use them - the companion website contains tutorials, R and Python codes, instructor materials including slides, exercises, and problems for students

Cyber-physical systems are the natural extension of the so-called "Internet of Things". They are "systems of collaborating computational elements controlling physical entities". Cyber Physical Systems of Systems (CPSoS) are considered "The Next Computing Revolution" after Mainframe computing (60's-70's), Desktop computing & Internet (80's-90's) and Ubiquitous computing (00's); because all aspects of daily life are rapidly evolving towards humans interacting amongst themselves as well as their environment via computational devices (often mobile), and because in most cases systems will employ their computational capabilities to interact amongst themselves. CPSoS enable the physical world to merge with the cyber one. Using sensors, the embedded systems monitor and collect data from physical processes, such as the steering of a vehicle, energy consumption or human health functions. The systems are networked making the data globally available. CPSoS make it possible for software applications to directly interact with events in the physical world, for example to measure and react to changes in blood pressure or peaks in energy consumption. Embedded hardware and software systems crucially expand the functionality and competitiveness of vehicles, aircraft, medical equipment, production plants and household appliances. Connecting these systems to a virtual environment of globally networked services and information systems opens completely new areas of innovation and novel business platforms. Future CPSoS will have many sophisticated, interconnected parts that must instantaneously exchange, parse, and act on detailed data in a highly coordinated manner. Continued advances in science and engineering will be necessary to enable advances in design and development of these complex systems. Multi- scale, multi-layer, multi-domain, and multi-system integrated infrastructures will require new foundations in system science and engineering. Scientists and engineers with an understanding of otherwise physical systems will need to work in tandem with computer and information scientists to achieve effective, workable designs. In this tutorial, basic and advanced issues on the design of the future heterogeneous CPSoS are presented including relevant Blockchain technologies, reconfigurable systems, advanced sensor interfaces and human-centered design processes. Certain advanced tools for the design and implementation of the cyber parts of the CPSoS (i.e. FPGA design tools from Xilinx) are also covered.

Focuses on the Internet of Healthcare Things and innovative solutions developed for use in the application of healthcare services Discusses artificial intelligence applications, experiments, core concepts, and cutting-edge themes Demonstrates new approaches to analysing medical data and identifying ailments using AI to improve overall quality of life Introduces fundamental concepts for designing the Internet of Healthcare Things solutions Includes pertinent case studies and applications

"A systematic review of the structure and context of the blockchain-derived economic model... (the book) describes cryptoeconomics in connection with the game theory, behavioral economics and others in simple understandable language."-Wang Feng, founder of Linekong Interactive Group and Mars Finance, partner in Geekbang Venture Capital Blockchain technology has subverted existing perceptions and is the start of an economic revolution, called, cryptoeconomics. Blockchain is a key component of cryptoeconomics. Vlad Zamfir, a developer of Ethereum, defines this term as "a formal discipline that studies protocols that governs the production, distribution, and consumption of goods and services in a decentralized digital economy. Cryptoeconomics is a practical science that focuses on the design and characterization of these protocols". This book explains the structures of blockchain-derived economic models, their history, and their application. It uses real-world cases to illustrate the relationship between cryptoeconomics and blockchain. Blockchain technology solves trust issues. A blockchain application can restrict behavior on the blockchain through a reward and punishment system that enables consensus in an innovative way. The greatest significance of cryptoeconomics lies in guaranteeing safety, stability, activity, and order in a decentralized consensus system. Security and stability are achieved mainly by cryptographical mechanisms. Activity and order are achieved through economic mechanisms. Cryptoeconomics and Blockchain: Ignighting a New Era of Blockchain discusses the most popular consensus algorithms and optimization mechanisms. With examples explained in clear and simple terms that are easy to understand, the book also explores economic mechanisms of blockchain such as game theory and behavioral economics.

In order to make informed decisions, there are three important elements: intuition, trust, and analytics. Intuition is based on experiential learning and recent research has shown that those who rely on their "gut feelings" may do better than those who don't. Analytics, however, are important in a data-driven environment to also inform decision making. The third element, trust, is critical for knowledge sharing to take place. These three elements-intuition, analytics, and trust-make a perfect combination for decision making. This book gathers leading researchers who explore the role of these three elements in the process of decision-making.

The growth of machines and users of the Internet has led to the proliferation of all sorts of data concerning individuals, institutions, companies, governments, universities, and all kinds of known objects and events happening everywhere in daily life. Scientific knowledge is not an exception to the data boom. The phenomenon of data growth in science pushes forth as the number of scientific papers published doubles every 9-15 years, and the need for methods and tools to understand what is reported in scientific literature becomes evident. As the number of academicians and innovators swells, so do the number of publications of all types, yielding outlets of documents and depots of authors and institutions that need to be found in Bibliometric databases. These databases are dug into and treated to hand over metrics of research performance by means of Scientometrics that analyze the toil of individuals, institutions, journals, countries, and even regions of the world. The objective of this book is to assist students, professors, university managers, government, industry, and stakeholders in general, understand which are the main Bibliometric databases, what are the key research indicators, and who are the main players in university rankings and the methodologies and approaches that they employ in producing ranking tables. The book is divided into two sections. The first looks at Scientometric databases, including Scopus and Google Scholar as well as institutional repositories. The second section examines the application of Scientometrics to world-class universities and the role that Scientometrics can play in competition among them. It looks at university rankings and the methodologies used to create these rankings. Individual chapters examine specific rankings that include: QS World University Scimago Institutions Webometrics U-Multirank U.S. News & World Report The book concludes with a discussion of university performance in the age of research analytics.

"Vivek Kale has written a great book on performance management that focuses on decision-making; on continuous, incremental improvement; and on identifying common patterns in becoming a more intelligent organization." -James Taylor, CEO of Decision Management Solutions and author of Real-World Decision Modeling with DMN "Introducing the concepts of decision patterns and performance intelligence, Vivek Kale has written another important book on the issues faced by contemporary organizations."-Gary Cokins, author of Predictive Business Analytics and Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics Enterprise Performance Intelligence and Decision Patterns unravels the mystery of enterprise performance intelligence (EPI) and explains how it can transform the operating context of business enterprises. It provides a clear understanding of what EPI means, what it can do, and application areas where it is practical to use. The need to be responsive to evolving customer needs and desires creates organizational structures where business intelligence (BI) and decision making is pushed out to operating units that are closest to the scene of the action. Closed-loop decision making resulting from a combination of on-going performance management with on-going BI can lead to an effective responsive enterprise; hence, the need for performance intelligence (PI). This pragmatic book: Introduces the technologies such as data warehousing, data mining, analytics, and business intelligence systems that are a first step toward enabling data-driven enterprises. Details decision patterns and performance decision patterns that pave the road for performance intelligence applications. Introduces the concepts, principles, and technologies related to performance measurement systems. Describes the concepts and principles related to balance scorecard systems (BCS). Introduces aspects of performance intelligence for the real-time enterprises. Enterprise Performance Intelligence and Decision Patterns shows how a company can design and implement instruments ranging from decision patterns to PI systems that can enable continuous correction of business unit behavior so companies can enhance levels of productivity and profitability.

This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions. The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners. This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

This book shows healthcare professionals how to turn data points into meaningful knowledge upon which they can take effective action. Actionable intelligence can take many forms, from informing health policymakers on effective strategies for the population to providing direct and predictive insights on patients to healthcare providers so they can achieve positive outcomes. It can assist those performing clinical research where relevant statistical methods are applied to both identify the efficacy of treatments and improve clinical trial design. It also benefits healthcare data standards groups through which pertinent data governance policies are implemented to ensure quality data are obtained, measured, and evaluated for the benefit of all involved. Although the obvious constant thread among all of these important healthcare use cases of actionable intelligence is the data at hand, such data in and of itself merely represents one element of the full structure of healthcare data analytics. This book examines the structure for turning data into actionable knowledge and discusses: The importance of establishing research questions Data collection policies and data governance Principle-centered data analytics to transform data into information Understanding the "why" of classified causes and effects Narratives and visualizations to inform all interested parties Actionable Intelligence in Healthcare is an important examination of how proper healthcare-related questions should be formulated, how relevant data must be transformed to associated information, and how the processing of information relates to knowledge. It indicates to clinicians and researchers why this relative knowledge is meaningful and how best to apply such newfound understanding for the betterment of all.

This book highlights the practical aspects of using Oracle Essbase and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. It explains the key steps involved in Oracle Essbase and OBIEE implementations. Using case studies, the book covers Oracle Essbase for analytical BI and data integration, using OBIEE for operational BI including presentation services and BI Publisher for real-time reporting services, Self-service BI- in terms of VLDB, scalability, high performance, stability, long-lasting and ease of use that saves time, effort, and costs, while maximizing ROI.

* Targests readers with a background in programming, interested in an introduction/refresher in statistical hypothesis testing * Uses Python throughout * Provides the reader with the opportunity of using the book whenever needed rather than following a sequential path.

DataOps is a new way of delivering data and analytics that is proven to get results. It enables IT and users to collaborate in the delivery of solutions that help organisations to embrace a data-driven culture. The DataOps Revolution: Delivering the Data-Driven Enterprise is a narrative about real world issues involved in using DataOps to make data-driven decisions in modern organisations. The book is built around real delivery examples based on the author's own experience and lays out principles and a methodology for business success using DataOps. Presenting practical design patterns and DataOps approaches, the book shows how DataOps projects are run and presents the benefits of using DataOps to implement data solutions. Best practices are introduced in this book through the telling of a story, which relates how a lead manager must find a way through complexity to turn an organisation around. This narrative vividly illustrates DataOps in action, enabling readers to incorporate best practices into everyday projects. The book tells the story of an embattled CIO who turns to a new and untested project manager charged with a wide remit to roll out DataOps techniques to an entire organisation. It illustrates a different approach to addressing the challenges in bridging the gap between IT and the business. The approach presented in this story lines up to the six IMPACT pillars of the DataOps model that Kinaesis (www.kinaesis.com) has been using through its consultants to deliver successful projects and turn around failing deliveries. The pillars help to organise thinking and structure an approach to project delivery. The pillars are broken down and translated into steps that can be applied to real-world projects that can deliver satisfaction and fulfillment to customers and project team members.

Blockchain Supply Chain Use Cases. Distributed Ledger Technology Supply Chain Use Cases. Blockchain-Enabled Digital Transformation Use Cases. Blockchain Supply Chain Diffusion/Innovation Use Cases.

Complete real-world examples of gathering feedback from users and web environments; Fundamentals of text analysis using JavaScript and PHP; Harnessing JavaScript data visualisation tools; Business focused application to feedback gathering, analysis and reporting; Integration of new and existing data sources into a single bespoke web-based analysis environment

Cognitive Computing for Internet of Medical Things (IoMT) offers a complete assessment of the present scenario, role, challenges, technologies, and impact of IoMT-enabled smart healthcare systems. It contains chapters discussing various biomedical applications under the umbrella of the IoMT. Key Features Exploits the different prospects of cognitive computing techniques for the IoMT and smart healthcare applications Addresses the significance of IoMT and cognitive computing in the evolution of intelligent medical systems for biomedical applications Describes the different computing techniques of cognitive intelligent systems from a practical point of view: solving common life problems Explores the technologies and tools to utilize IoMT for the transformation and growth of healthcare systems Focuses on the economic, social, and environmental impact of IoMT-enabled smart healthcare systems This book is primarily aimed at graduates, researchers and academicians working in the area of development of the application of the of the application of the IoT in smart healthcare. Industry professionals will also find this book helpful.

Discusses the efficiency measurement of online education Presents the environmental impact of online education Offers a parametric evaluation and categorization of online learning systems Covers big data ecosystems in cloud computing Provides analytical methods to find solutions for big data challenges

This new book aims to provide to both beginners and experts with a completely algorithmic approach to data analysis and conceptual modeling, database design, implementation, and tuning, starting from vague and incomplete customer requests and ending with IBM DB/2, Oracle, MySQL, MS SQL Server, or Access based software applications. A rich panoply of solutions to actual useful data sub-universes (e.g. business, university, public and home library, geography, history, etc.) is provided, constituting a powerful library of examples. Four data models are presented and used: the graphical Entity-Relationship, the mathematical EMDM, the physical Relational, and the logical deterministic deductive Datalogones. For each one of them, best practice rules, algorithms, and the theory beneath are clearly separated. Four case studies, from a simple public library example, to a complex geographical study are fully presented, on all needed levels. Several dozens of real life exercises are proposed, out of which at least one per chapter is completely solved. Both major historical and up-to-date references are provided for each of the four data models considered. The book provides a library of useful solutions to real-life problems and provides valuable knowledge on data analysis and modeling, database design, implementation, and fine tuning.

A pathbreaking history of art that uses digital research and economic tools to reveal enduring inequities in the formation of the art historical canon Painting by Numbers presents a groundbreaking blend of art historical and social scientific methods to chart, for the first time, the sheer scale of nineteenth-century artistic production. With new quantitative evidence for more than five hundred thousand works of art, Diana Seave Greenwald provides fresh insights into the nineteenth century, and the extent to which art historians have focused on a limited-and potentially biased-sample of artwork from that time. She addresses long-standing questions about the effects of industrialization, gender, and empire on the art world, and she models more expansive approaches for studying art history in the age of the digital humanities. Examining art in France, the United States, and the United Kingdom, Greenwald features datasets created from indices and exhibition catalogs that-to date-have been used primarily as finding aids. From this body of information, she reveals the importance of access to the countryside for painters showing images of nature at the Paris Salon, the ways in which time-consuming domestic responsibilities pushed women artists in the United States to work in lower-prestige genres, and how images of empire were largely absent from the walls of London's Royal Academy at the height of British imperial power. Ultimately, Greenwald considers how many works may have been excluded from art historical inquiry and shows how data can help reintegrate them into the history of art, even after such pieces have disappeared or faded into obscurity. Upending traditional perspectives on the art historical canon, Painting by Numbers offers an innovative look at the nineteenth-century art world and its legacy.

The book IoT and Big Data Analytics (IoT-BDA) for Smart Cities - A Global Perspective, emphasizes the challenges, architectural models, and intelligent frameworks with smart decisionmaking systems using Big Data and IoT with case studies. The book illustrates the benefits of Big Data and IoT methods in framing smart systems for smart applications. The text is a coordinated amalgamation of research contributions and industrial applications in the field of smart cities. Features: Provides the necessity of convergence of Big Data Analytics and IoT techniques in smart city application Challenges and Roles of IoT and Big Data in Smart City applications Provides Big Data-IoT intelligent smart systems in a global perspective Provides a predictive framework that can handle the traffic on abnormal days, such as weekends and festival holidays Gives various solutions and ideas for smart traffic development in smart cities Gives a brief idea of the available algorithms/techniques of Big Data and IoT and guides in developing a solution for smart city applications This book is primarily aimed at IT professionals. Undergraduates, graduates, and researchers in the area of computer science and information technology will also find this book useful.

Enterprise Level Security 2: Advanced Topics in an Uncertain World follows on from the authors' first book on Enterprise Level Security (ELS), which covered the basic concepts of ELS and the discoveries made during the first eight years of its development. This book follows on from this to give a discussion of advanced topics and solutions, derived from 16 years of research, pilots, and operational trials in putting an enterprise system together. The chapters cover specific advanced topics derived from painful mistakes and numerous revisions of processes. This book covers many of the topics omitted from the first book including multi-factor authentication, cloud key management, enterprise change management, entity veracity, homomorphic computing, device management, mobile ad hoc, big data, mediation, and several other topics. The ELS model of enterprise security is endorsed by the Secretary of the Air Force for Air Force computing systems and is a candidate for DoD systems under the Joint Information Environment Program. The book is intended for enterprise IT architecture developers, application developers, and IT security professionals. This is a unique approach to end-to-end security and fills a niche in the market.