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We are living in the midst of a social, economic, and technological revolution. How we communicate, socialize, spend leisure time, and conduct business has moved onto the Internet. The Internet has in turn moved into our phones, into devices spreading around our homes and cities, and into the factories that power the industrial economy. The resulting explosion of data and discovery is changing our world. Most definitions of big data reflect the growing technological ability to capture, aggregate, and process an ever-greater volume, velocity, and variety of data. Big data will transform the way we live and work and alter the relationships between government, citizens, businesses, and consumers. This book focuses on how the public and private sectors can maximize the benefits of big data while minimizing its risks. It also identifies opportunities for big data to grow our economy, improve health and education, and make our nation safer and more energy efficient. The book continues by exploring the changing nature of privacy as computing technology has advanced and big data has come to the forefront; identifying the sources of these data, the utility of these data; the privacy challenges big data poses in a world where technologies for re-identification often outpace privacy-preserving de-identification capabilities; and where it is increasingly hard to identify privacy-sensitive information at the time of its collection.
(source: Nielsen Book Data)

The ultimate guide to managing, building, and deploying large-scale clusters with Apache Mesos About This Book * Master the architecture of Mesos and intelligently distribute your task across clusters of machines * Explore a wide range of tools and platforms that Mesos works with * This real-world comprehensive and robust tutorial will help you become an expert Who This Book Is For The book aims to serve DevOps engineers and system administrators who are familiar with the basics of managing a Linux system and its tools What You Will Learn * Understand the Mesos architecture * Manually spin up a Mesos cluster on a distributed infrastructure * Deploy a multi-node Mesos cluster using your favorite DevOps * See the nuts and bolts of scheduling, service discovery, failure handling, security, monitoring, and debugging in an enterprise-grade, production cluster deployment * Use Mesos to deploy big data frameworks, containerized applications, or even custom build your own applications effortlessly In Detail Apache Mesos is open source cluster management software that provides efficient resource isolations and resource sharing distributed applications or frameworks. This book will take you on a journey to enhance your knowledge from amateur to master level, showing you how to improve the efficiency, management, and development of Mesos clusters. The architecture is quite complex and this book will explore the difficulties and complexities of working with Mesos. We begin by introducing Mesos, explaining its architecture and functionality. Next, we provide a comprehensive overview of Mesos features and advanced topics such as high availability, fault tolerance, scaling, and efficiency. Furthermore, you will learn to set up multi-node Mesos clusters on private and public clouds. We will also introduce several Mesos-based scheduling and management frameworks or applications to enable the easy deployment, discovery, load balancing, and failure handling of long-running services. Next, you will find out how a Mesos cluster can be easily set up and monitored using the standard deployment and configuration management tools. This advanced guide will show you how to deploy important big data processing frameworks such as Hadoop, Spark, and Storm on Mesos and big data storage frameworks such as Cassandra, Elasticsearch, and Kafka. Style and approach This advanced guide provides a detailed step-by-step account of deploying a Mesos cluster. It will demystify the concepts behind Mesos.
(source: Nielsen Book Data)

Learn how to leverage the power of R for Business Intelligence About This Book * Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful. * This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R. * Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide. Who This Book Is For This book is for data analysts, business analysts, data science professionals or anyone who wants to learn analytic approaches to business problems. Basic familiarity with R is expected. What You Will Learn * Extract, clean, and transform data * Validate the quality of the data and variables in datasets * Learn exploratory data analysis * Build regression models * Implement popular data-mining algorithms * Visualize results using popular graphs * Publish the results as a dashboard through Interactive Web Application frameworks In Detail Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence. Style and approach This book will take a step-by-step approach and instruct you in how you can achieve Business Intelligence from scratch using R. We will start with extracting data and then move towards exploring, analyzing, and visualizing it. Eventually, you will learn how to create insightful dashboards that help you make informed decisions-and all of this with the help of real-life examples.
(source: Nielsen Book Data)

• Chapter 1 SC-MCHMP: Score-Based Cluster Level Hybrid Multi-Channel MAC Protocol for Wireless Sensor Network
• Chapter 2 Software-Defined Networking (SDN) Security Concerns
• Chapter 3 Clustering in Wireless Sensor Networks Using Adaptive Neuro-Fuzzy Inference Logic
• Chapter 4 Security in Big Data
• Chapter 5 Prevention of DOS/DDOS Attacks Through Expert Honey-Mesh Security Infrastructure
• Chapter 6 Efficient Feature Grouping for IDS Using Clustering Algorithms in Detecting Known/Unknown Attacks
• Chapter 7 PDF Malware Classifiers - A Survey, Future Directions, and Recommended Methodology
• Chapter 8 Key Authentication Schemes for Medical Cyber Physical System
• Chapter 9 Ransomware Attack: Threats & Different Detection Technique
• Chapter 10 Security Management System (SMS)
• Chapter 11 Automatic Street Light Control Based on Pedestrian and Automobile Detection
• Chapter 12 Cost-Oriented Electronic Voting System Using Hashing Function with Digital Persona
• Chapter 13 Blockchain-Based Supply Chain System Using Intelligent Chatbot with IoT-RFID.
• (source: Nielsen Book Data)

Addresses the knowledge for emerging multidisciplinary research Explores basic and high level concepts and serves as a manual for industry Presents security and privacy issues through IoT ecosystems and its implications to the real-world Explains concepts of IoT related technologies, trends, and future directions related to information security Examines privacy issues and challenges related to data-intensive technologies in IoT.
(source: Nielsen Book Data)

• Table of Contents Introduction to Healthcare Analytics Healthcare Foundations Machine Learning Foundations Computing Foundations - Databases Computing Foundations - Introduction to Python Measuring Healthcare Quality Making Predictive Models in Healthcare Healthcare Predictive Models - A Review The Future - Healthcare and Emerging Technologies.
• (source: Nielsen Book Data)

Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book DescriptionIn recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists' work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is forHealthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.
(source: Nielsen Book Data)

Learn how to leverage the power of R for Business Intelligence About This Book * Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful. * This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R. * Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide. Who This Book Is For This book is for data analysts, business analysts, data science professionals or anyone who wants to learn analytic approaches to business problems. Basic familiarity with R is expected. What You Will Learn * Extract, clean, and transform data * Validate the quality of the data and variables in datasets * Learn exploratory data analysis * Build regression models * Implement popular data-mining algorithms * Visualize results using popular graphs * Publish the results as a dashboard through Interactive Web Application frameworks In Detail Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence. Style and approach This book will take a step-by-step approach and instruct you in how you can achieve Business Intelligence from scratch using R. We will start with extracting data and then move towards exploring, analyzing, and visualizing it. Eventually, you will learn how to create insightful dashboards that help you make informed decisions-and all of this with the help of real-life examples.
(source: Nielsen Book Data)

• Machine generated contents note: pt. I Social and Psychological Perspectives on Sensitivity and Meaning With an introduction / Scott Slovic
• 1. More Who Die, the Less We Care: Psychic Numbing and Genocide / Daniel Vastfjall
• 2. Pseudoinefficacy and the Arithmetic of Compassion / Marcus Mayorga
• 3. Prominence Effect: Confronting the Collapse of Humanitarian Values in Foreign Policy Decisions / Paul Slovic
• 4. Age of Numbing / Greg Mitchell
• 5. Epidemic Disease as Structural Violence: An Excerpt from Never Again? Reflections on Human Values and Human Rights / Paul Farmer
• pt. II Narrative, Analytical, and Visual Strategies for Prompting Sensitivity and Meaning With an introduction / Paul Slovic
• 6. Power of One / Nicholas D. Kristof
• 7. From One to Too Many / Kenneth Helphand
• 8. Wreck of Time / Annie Dillard
• 9. Science, Eloquence, and the Asymmetry of Trust: What's at Stake in Climate Change Fiction / Scott Slovic
• 10. Healing Rwanda / Terry Tempest Williams
• 11. When Words Fail: Climate Change Activists Have Chosen a Magic Number / Bill Mckibben
• 12. Blood Root of Art / Rick Bass
• pt. III Interviews on the Communication of Numerical Information to the General Public With an introduction / Paul Slovic
• 13. Reacting to Information in a "Personal, Moral Way": An Interview with Homero and Betty Aridjis
• 14. Countering the "Anesthesia of Destruction": An Interview with Vandana Shiva
• 15. Meaning of "One Data Point": An Interview with Sandra Steingraber
• 16. Introspection, Social Transformation, and the Trans-Scalar Imaginary: An Interview with Chris Jordan
• pt. IV Postscript / Paul Slovic.

"We live in the age of Big Data, awash in a sea of ever-expanding information--a constant deluge of facts, statistics, models, and projections. The human mind is quickly desensitized by information presented in the form of numbers, and yet many important social and environmental phenomena, ranging from genocide to global climate change, require quantitative description. The essays and interviews in Numbers and Nerves explore the quandary of our cognitive responses to quantitative information, while also offering compelling strategies for overcoming insensitivity to the meaning of such information. With contributions by journalists, literary critics, psychologists, naturalists, activists, and others, this book represents a unique convergence of psychological research, discourse analysis, and visual and narrative communication. At a time of unprecedented access to information, our society is frequently stymied in its efforts to react to the world's massive problems. Many of these problems are systemic, deeply rooted in seemingly intransigent cultural patterns and lifestyles. In order to sense the significance of these issues and begin to confront them, we must first understand the psychological tendencies that enable and restrict our processing of numerical information. Numbers and Nerves explores a wide range of psychological phenomena and communication strategies--fast and slow thinking, psychic numbing, pseudoinefficacy, the prominence effect, the asymmetry of trust, contextualized anecdotes, multifaceted mosaics of prose, and experimental digital compositions, among others--and places these in real-world contexts. In the past two decades, cognitive science has increasingly come to understand that we, as a species, think best when we allow numbers and nerves, abstract information and experiential discourse, to work together. This book provides a roadmap to guide that collaboration. It will be invaluable to scholars, educators, professional communicators, and anyone who struggles to grasp the meaning behind the numbers."-- Provided by publisher

• Authors. Part I Origin and Background of COVID-19.
• Chapter 1 Introduction to Emerging Respiratory Viruses with Coronavirus Disease (COVID-19).
• Chapter 2 The Origin Molecular Structure, Function, and Evolution Insights of COVID-19: Morphogenesis and Spike Proteins. Part II COVID-19 Screening, Testing and Detection Systems: Different Paths to the Same Destination.
• Chapter 3 Real Time-Polymerase Chain Reaction (RT-PCR) and Antibody Test.
• Chapter 4 Antigen-Antibody Reaction-Based Immunodiagnostics Method. Part III COVID-19 Detection: Advanced Image Processing with Artificial Intelligence Techniques.
• Chapter 5 Lung Function Testing (LFT) with Normal CT Scans and AI Algorithm.
• Chapter 6 Chest X-Ray Image-Based Testing Using Machine Learning Techniques.
• Chapter 7 Blood Cell Microscope Image-Based Testing Using Deep Learning Techniques. Part IV Analysis of the Pre- and Post-Impact of the COVID-19 Pandemic Crisis.
• Chapter 8 Direct and Indirect Impacts of Environmental Factors on the COVID-19 Pandemic.
• Chapter 9 Direct and Indirect Impacts of the COVID-19 Pandemic Crisis on Economy.
• Chapter 10 Direct and Indirect Impacts of the COVID-19 Pandemic Crisis on Food & Agriculture.
• Chapter 11 Direct and Indirect Impacts of the COVID-19 Pandemic Crisis on Hotels, Tour and Travel Sectors.
• Chapter 12 Direct and Indirect Impacts of the COVID-19 Pandemic Crisis on Human Physical and Physiological Health. Index.
• (source: Nielsen Book Data)

This book highlights progress in terms of Virus Biology and Infection Detection, Prevention, and Control, along with Screening, Testing, and Detection Techniques, that will provide learners and researchers (from basic to advanced) with the most innovative computer-driven methodologies for the fight against COVID-19. In addition, this book also covers the Pre- and Post-Impact of the COVID-19 Pandemic Crisis that will definitely provide useful content for researchers to think broadly about the analytical areas affected by COVID-19. This ultimately shows different paths to the same destination to help understand the nature of the COVID-19 pandemic and how to avoid it in the future.
(source: Nielsen Book Data)

The ultimate guide to managing, building, and deploying large-scale clusters with Apache Mesos About This Book * Master the architecture of Mesos and intelligently distribute your task across clusters of machines * Explore a wide range of tools and platforms that Mesos works with * This real-world comprehensive and robust tutorial will help you become an expert Who This Book Is For The book aims to serve DevOps engineers and system administrators who are familiar with the basics of managing a Linux system and its tools What You Will Learn * Understand the Mesos architecture * Manually spin up a Mesos cluster on a distributed infrastructure * Deploy a multi-node Mesos cluster using your favorite DevOps * See the nuts and bolts of scheduling, service discovery, failure handling, security, monitoring, and debugging in an enterprise-grade, production cluster deployment * Use Mesos to deploy big data frameworks, containerized applications, or even custom build your own applications effortlessly In Detail Apache Mesos is open source cluster management software that provides efficient resource isolations and resource sharing distributed applications or frameworks. This book will take you on a journey to enhance your knowledge from amateur to master level, showing you how to improve the efficiency, management, and development of Mesos clusters. The architecture is quite complex and this book will explore the difficulties and complexities of working with Mesos. We begin by introducing Mesos, explaining its architecture and functionality. Next, we provide a comprehensive overview of Mesos features and advanced topics such as high availability, fault tolerance, scaling, and efficiency. Furthermore, you will learn to set up multi-node Mesos clusters on private and public clouds. We will also introduce several Mesos-based scheduling and management frameworks or applications to enable the easy deployment, discovery, load balancing, and failure handling of long-running services. Next, you will find out how a Mesos cluster can be easily set up and monitored using the standard deployment and configuration management tools. This advanced guide will show you how to deploy important big data processing frameworks such as Hadoop, Spark, and Storm on Mesos and big data storage frameworks such as Cassandra, Elasticsearch, and Kafka. Style and approach This advanced guide provides a detailed step-by-step account of deploying a Mesos cluster. It will demystify the concepts behind Mesos.
(source: Nielsen Book Data)

Build, implement and scale distributed deep learning models for large-scale datasets About This Book * Get to grips with the deep learning concepts and set up Hadoop to put them to use * Implement and parallelize deep learning models on Hadoop's YARN framework * A comprehensive tutorial to distributed deep learning with Hadoop Who This Book Is For If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book. What You Will Learn * Explore Deep Learning and various models associated with it * Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it * Implement Convolutional Neural Network (CNN) with deeplearning4j * Delve into the implementation of Restricted Boltzmann Machines (RBM) * Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN) * Get hands on practice of deep learning and their implementation with Hadoop. In Detail This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance. Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann Machines and autoencoder using the popular deep learning library deeplearning4j. Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop. By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop. Style and approach This book takes a comprehensive, step-by-step approach to implement efficient deep learning models on Hadoop. It starts from the basics and builds the readers' knowledge as they strengthen their understanding of the concepts. Practical examples are included in every step of the way to supplement the theory.
(source: Nielsen Book Data)

Take tiny steps to enter the big world of data science through this interesting guide About This Book * Learn the fundamentals of machine learning and build your own intelligent applications * Master the art of building your own machine learning systems with this example-based practical guide * Work with important classification and regression algorithms and other machine learning techniques Who This Book Is For This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed. What You Will Learn * Exploit the power of Python to handle data extraction, manipulation, and exploration techniques * Use Python to visualize data spread across multiple dimensions and extract useful features * Dive deep into the world of analytics to predict situations correctly * Implement machine learning classification and regression algorithms from scratch in Python * Be amazed to see the algorithms in action * Evaluate the performance of a machine learning model and optimize it * Solve interesting real-world problems using machine learning and Python as the journey unfolds In Detail Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms - they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. Style and approach This book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on work-giving you a deep insight into the world of machine learning. With simple yet rich language-Python-you will understand and be able to implement the examples with ease.
(source: Nielsen Book Data)

• Introduction xxiii Part I Introduction to Data Visualization 1 1 Fundamentals of Visualization 3 Data Visualization versus Artistic Visualization 4 The Place of Infographics 7 Using 3D Effectively 7 The Illusion of Depth 8 Additional Dimensions 9 A Description of the Problem and a Proposed Solution 10 Summary 11 2 Designing a Visualization 13 Goals of Visualization 13 Human Perceptual Abilities 15 Strategic, Tactical, and Operational Views 18 Glance and Go versus Data Exploration 21 Using Color in Visualizations 23 Use of Perspective and Shape 28 Summary 31 Part II Microsoft s Toolset for Visualizing Data 33 3 The Microsoft Toolset 35 A Brief History 35 Database Tools 40 The Place of Each Front-End Tool 44 Installing the Sample Databases 46 Summary 51 4 Building Data Sets to Support Visualization 53 What Data Sets Are 53 Why We Need Them 53 How Data Sets Are Created 54 Why Data Sets Are Important 54 Common Data Set Elements 54 Data Quality 55 Metadata 55 Formatting 56 Data Volume 56 Automated Data 57 Types of Data Sets and Sources 57 Data in the Internet Age 58 Spreadsheets 58 When to Store Data in a Spreadsheet 58 SQL Tables 59 OLAP and Tabular Models 60 Reports and Data Feeds 60 Hadoop and Other Nonrelational Sources 61 Creating Data Sets for Visualization 62 Copy and Paste 62 Exporting Data from Systems 62 Import Techniques and Tools 62 Getting Started 63 Your First Data Set 63 Your First Data Set 63 Getting Data 64 Cleaning Your Data 65 Moving Your Data into a Good Format for Visualization 66 Verifying Your Data by Prototyping 67 Summary 68 5 Excel and PowerPivot 69 What are Excel and PowerPivot? 69 PowerPivot versus BISM versus Analysis Services 69 Column Stores 73 Multidimensional versus In Memory Models 75 Creating Your First PowerPivot Model 75 Step
• 1: Understand Your Data 76 Step
• 2: Create Your First Model 76 Step
• 3: Does Your Model Work? 81 What Does Excel Do for Me? 82 Pivot Charts and Tables 82 Summary 86 6 Power View 87 What Is Power View? 87 BISM: The First Requirement for Power View 90 Creating a Power View Report 91 Creating a Data Source 91 Creating a New Power View Report in Excel 91 Enhancing Data Models for Power View 97 Cleaning Up Your Data Model 97 Adding Metadata for Power View 98 Sharing Power View Reports 100 Publish in SharePoint 101 Exporting to PowerPoint 103 Installing the Power View Samples 103 Summary 104 7 PerformancePoint 105 Tabular versus Multidimensional Sources 105 Requirements for Running PerformancePoint 106 SharePoint Requirements 106 Authentication Issues when Using Secure Store Service 108 KPIs, Scorecards, Filters, Reports, and Dashboards 110 Creating a Data Source 110 Mapping the Time Dimension 112 KPIs 118 Scorecards 120 Filters 121 Analytic Reports 122 Dashboards 123 Combining Visualizations in PerformancePoint 124 Embedding an SSRS Report 125 Embedding Excel Reports 125 Creating Web Part Pages in SharePoint 126 Adding Web Parts 127 PerformancePoint Connections 128 Installing the PerformancePoint Samples 130 Summary 132 8 Reporting Services 133 Native versus Integrated Mode 133 Native Mode 134 SharePoint Integrated Mode 134 Shared and Embedded Data Sources 136 Authentication: A Better Solution 137 The Double Hop Problem 138 Set Execution Context: Requirements and Setup 138 Expressions in Reporting Services 140 Business Intelligence Development Studio and Visual Studio versus Report Builder 141 Installing the Reporting Services Samples 145 Summary 146 9 Custom Code 147 Silverlight, WPF, XAML, and HTML5 147 The Future of Silverlight 150 Accessing Data from HTML5 151 Installing the HTML5 samples 152 A Web Service Sample in C# 154 Summary 167 Part III Visual Analytics in Practice 169 10 Scorecards and Indicators 171 A Quick Understanding: Glance and Go 172 KPIs 172 Drill Down 173 Drill Through 174 Drill Across 174 Tool Choices, with Examples 175 PerformancePoint 175 Excel 177 Implementation Examples 179 Implementing a Scorecard in Excel 179 PerformancePoint Services (PPS) Scorecard: Traffic Lights 182 Custom Indicators in PerformancePoint 187 Summary 189 11 Timelines 191 Types of Temporal Analysis Visualization 192 Timelines 194 Line Charts 195 Bar and Column Charts 197 Combined Charting 198 Scatter Plots and Bubble Charts 199 Tiling 200 Animation 201 Tool Choices, with Examples 202 PerformancePoint Services (PPS) 202 SQL Server Reporting Services (SSRS) 205 Excel 208 Power View 210 Implementation Examples 214 Power View Animated Scatter Plot 214 Combining Lines and Columns in Excel 218 A Drillable Line Chart in PPS 222 A Data-Driven Timeline Using SSRS and Data Bars 223 Summary 226 12 Comparison Visuals 227 Overview of Point-in-Time Comparisons 227 Explaining Perspective and Perceiving Comparisons 229 Pie Charts Versus Bar Charts 230 Bullet Charts 231 Radar Charts 232 Matrices 233 Custom Comparisons 236 Tool Choices, with Examples 237 PerformancePoint Services 237 SSRS 238 Excel 238 Power View 240 HTML5 241 Implementation Examples 242 PerformancePoint: Column Graphs 242 Excel: Multiple Axes and Scale Breaks 244 Excel: Radar Charts 252 SSRS: A Bullet Chart 254 HTML5 261 Summary 267 13 Slice and Dice: Ad Hoc Analytics 269 Explanation of Terms 270 Self-Service BI 270 The Place of PowerPivot 271 Definitions 272 Tool Choices with Examples 278 PerformancePoint: Analytic Charts 278 PerformancePoint: Drill Across 280 Excel Pivot Tables 281 SSRS Drill Down and Drill Through 282 Power View 283 Implementation Examples 284 SSRS: Dynamic Measures 284 Integrating PPS and SSRS on a Single Page 290 Power View: Exploring Data 295 Summary 298 14 Relationship Analysis 299 Visualizing Relationships: Nodes, Trees, and Leaves 299 Network Maps 301 Color Wheel 302 Tree Structures: Organization Charts and Other Hierarchies 305 Strategy Maps 306 Tool Choices 307 PPS Decomposition Tree 307 Excel and NodeXL 308 PerformancePoint Services (PPS) Strategy Maps 309 HTML5 Structure Maps 310 Implementation Examples 311 Building an Organization Chart in PerformancePoint 311 Building a Network Map in HTML5 316 Color Wheel in HTML5 317 Summary 320 15 Embedded Visualizations 321 Tabular Data: Adding Visual Acuity 322 Embedded Charts: Sparklines and Bars 323 Conditional Formatting 325 Indicators 326 Bullet Graphs 327 Tool Choices with Examples 329 Excel 329 SQL Server Reporting Services (SSRS) 330 PerformancePoint 331 Implementation Examples 331 Embedding Visualizations on a Pivot Table 331 Summary 339 16 Other Visualizations 341 Traditional Infographics 341 Periodic Tables 342 Swim Lanes 344 Transportation Maps 344 Mind Maps 347 Venn Diagram 347 CAD Drawings 348 3D Modeling 349 Funnels 349 Flow Diagrams 350 Geographic Information System Maps 352 Heatmaps 355 Summary 355 A Choosing a Microsoft Tool 357 Strengths and Weaknesses of Each Tool 357 PerformancePoint 357 Reporting Services 358 Excel/Excel Services 359 Power View 360 HTML5 361 Matching a Visualization to a Tool 362 B DAX Function Reference 369 Date and Time Functions 369 Filter Functions 371 Information Functions 372 Lookup Functions 373 Parent-Child Functions 373 Logical Functions 374 Text Functions 375 Statistical Functions 378 Math and Trig Functions 379 Time Intelligence Functions 381 Index 385.
• (source: Nielsen Book Data)

Go beyond design concepts and learn to build state-of-the-art visualizations The visualization experts at Microsoft's Pragmatic Works have created a full-color, step-by-step guide to building specific types of visualizations. The book thoroughly covers the Microsoft toolset for data analysis and visualization, including Excel, and explores best practices for choosing a data visualization design, selecting tools from the Microsoft stack, and building a dynamic data visualization from start to finish. You'll examine different types of visualizations, their strengths and weaknesses, and when to use each one. * Data visualization tools unlock the stories within the data, enabling you to present it in a way that is useful for making business decisions * This full-color guide introduces data visualization design concepts, then explains the various Microsoft tools used to store and display data * Features a detailed discussion of various classes of visualizations, their uses, and the appropriate tools for each * Includes practical implementations of various visualizations and best practices for using them * Covers out-of-the-box Microsoft tools, custom-developed illustrations and implementations, and code examples Visual Intelligence: Microsoft Tools and Techniques for Visualizing Data arms you with best practices and the knowledge to choose and build dynamic data visualizations.
(source: Nielsen Book Data)

Remain highly competitive in the server and VM market by gaining the practical skills needed to operate Nano Server About This Book * The days of the local server are numbered, and this book will make you an ace by giving you the skills needed to administer Nano Server and survive in the brave new server world * Learn to quickly automate multiple VMs and support Hyper-V clusters, all through small footprints from a single host * Apply up-to-date, real-world examples presented in this book and improve the scalability and efficiency of large-scale VM deployments Who This Book Is For This book opens up new potential for both developers and IT pros alike. The book is primarily for Server administrators and IT Professionals who would like to deploy and administer Nano Server within their organizations, and for developers who are trying to make maximal use of Server Containers and Hyper-V Containers with Nano Servers. What You Will Learn * Understand Nano Server * Deploy Nano Server * Deploy Hyper-V Clusters on Nano Server * Deploy Nano Server with SCVMM * Manage Nano Server using PowerShell and Remote Server Management Tools * Manage Nano Server with third-party tools * Run Server Containers and Hyper-V Containers on Nano Server * Troubleshoot Nano Server * Validate developed applications that run on Nano Server In Detail Nano Server allows developers and operations teams to work closely together and use containers that package applications so that the entire platform works as one. The aim of Nano Server is to help applications run the way they are intended to. It can be used to run and deploy infrastructures (acting as a compute host, storage host, container, or VM guest operating system) without consuming significant resources. Although Nano Server isn't intended to replace Server 2016 or 2012 R2, it will be an attractive choice for developers and IT teams. Want to improve your ability to deploy a new VM and install and deploy container apps within minutes? You have come to the right place! The objective of this book is to get you started with Nano Server successfully. The journey is quite exciting, since we are introducing you to a cutting-edge technology that will revolutionize today's datacenters. We'll cover everything from the basic to advanced topics. You'll discover a lot of added value from using Nano Server, such as hundreds of VM types on a single host through a small footprint, which could be a big plus for you and your company. After reading this book, you will have the necessary skills to start your journey effectively using Nano Server. Style and approach Gauge all the information needed to get up-and-running with the latest Nano Server built by Microsoft using this easy to follow step-by-step guide.
(source: Nielsen Book Data)