Tabla de Contenidos: “…Ecosystem value -- Measuring success -- Chapter summary -- CHAPTER 6 Systems of intelligence -- The rise of machine learning -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- The perfect companion to your data -- Five crucial factors in applying AI successfully -- Creating a system of intelligence -- Understand -- Identify -- Rank and plan -- Execute and monitor -- Assembling your team -- The five Vs of big data -- Constructing a highly defensible moat -- Vertical expertise -- Personalisation at scale -- Man and machine -- Network intelligence -- Integration intelligence -- Chapter summary -- Part III Accelerating change -- CHAPTER 7 Investment management -- The three types of innovation -- Disruptive innovation -- Sustaining innovation -- Efficiency-BASE D innovation -- Balancing short-term returns and long-term investments -- The investor's mindset -- Create a culture of innovation -- Move faster -- Encourage investment efficiency -- Chapter summary -- CHAPTER 8 Technology risk management -- Navigating the new rules of cybersecurity…”

Tabla de Contenidos: “…Ruiz-Morales -- La utilización del Big Data para la prevención y lucha contra el crimen / Javier Valls Prieto -- Aproximación a la naturaleza jurídica de las infraestructuras críticas: delineando las bases para la ciberseguridad peruana / Angel Jeancarlo Coaquira Flores -- Nueva propuesta de Reglamento del Parlamento Europeo y del Consejo para la prevención de la difusión de contenidos terroristas en línea / Francisco Salvador Gil García -- Internet of things y computación en nube: perspectivas en ciberseguridad a través del marco normativo con vistas desde el derecho internacional privado / Antonio Merchán Murillo -- Suiza y terrorismo: desafíos del código penal y TICs / Eltjon Mirashi Las consecuencias júridicas de las brechas de seguridad de datos personales / Alejandro Platero Alcón…”

Tabla de Contenidos: “…-- Use Cases for Azure Synapse Analytics -- Financial Services -- Manufacturing -- Retail -- Healthcare -- Reference Architectures for Azure Synapse Analytics -- Modern Data Warehouse Architecture -- Real-Time Analytics on Big Data Architecture -- Summary -- Index…”

Tabla de Contenidos: “…Fault tree analysis -- Threat modeling -- STRIDE threat model -- DREAD threat model -- Trustworthiness of an IIoT system -- Industrial big data pipeline and architectures -- Industrial IoT security architecture -- Business viewpoint -- Usage viewpoint -- Functional viewpoint -- Implementation viewpoint -- IIoT architecture patterns -- Pattern 1 - Three-tier architectural model -- Pattern 2 - Layered databus architecture -- Building blocks of industrial IoT security architecture -- A four-tier IIoT security model -- Summary -- Chapter 3: IIoT Identity and Access Management -- A primer on identity and access control -- Identification -- Authentication -- Authorization -- Account management -- Distinguishing features of IAM in IIoT -- Diversity of IIoT endpoints -- Resource-constrained and brownfield considerations -- Physical safety and reliability -- Autonomy and scalability -- Event logging is a rarity -- Subscription-based models -- Increasing sophistication of identity attacks -- Risk-based access control policy -- Identity management across the device lifecycle -- Authentication and authorization frameworks for IIoT -- Password-based authentication -- Biometrics -- Multi-factor authentication -- Key-based authentication -- Symmetric keys -- Asymmetric keys -- Zero-knowledge keys -- Certificate-based authentication -- Trust models - public key infrastructures and digital certificates -- PKI certificate standards for IIoT -- ITU-T X.509 -- IEEE 1609.2 -- Certificate management in IIoT deployments -- Extending the OAuth 2.0 authorization framework for IoT access control -- IEEE 802.1x -- Identity support in messaging protocols -- MQTT -- CoAP -- DDS -- REST -- Monitoring and management capabilities -- Activity logging support -- Revocation support and OCSP -- Building an IAM strategy for IIoT deployment -- Risk-based policy management -- Summary…”

Tabla de Contenidos: “…Cover -- Engage, Assess, Reflect -- Title Page -- Copyright Page -- Dedication -- Brief Contents -- Contents -- Preface -- Acknowledgments -- Global Edition Acknowledgments -- About the Authors -- Part 1: An Introduction -- Chapter 1: Welcome to the World of OB -- Chapter Warm-up -- Management and Organizational Behavior -- Organizational Behavior (OB) Defined -- Effective versus Successful Managerial Activities -- Watch It-Herman Miller: Organizational Behavior -- Complementing Intuition with Systematic Study -- Big Data -- Disciplines that Contribute to the OB Field -- Psychology -- Social Psychology -- Sociology -- Anthropology -- There are few Absolutes in OB -- Challenges and Opportunities for OB -- Continuing Globalization -- Workforce Demographics -- Workforce Diversity -- Social Media -- Employee Well-Being at Work -- Positive Work Environment -- Ethical Behavior -- Coming Attractions: Developing an OB Model -- Overview -- Inputs -- Processes -- Outcomes -- Summary -- Implications for Managers -- Personal Inventory Assessments: Multicultural Awareness Scale -- Part 2: Individual Differences -- Chapter 2: Attitudes -- Chapter Warm-up -- Attitudes -- Watch It-Gawker Media: Attitudes and Job Satisfaction -- Attitudes and Behavior -- Job Attitudes -- Job Satisfaction and Job Involvement -- Organizational Commitment -- Perceived Organizational Support -- Employee Engagement -- Measuring Job Satisfaction -- Approaches to Measurement -- Measured Job Satisfaction Levels -- What Causes Job Satisfaction? …”

Tabla de Contenidos: “…. -- Chapter 7: Handling Big Data -- Introduction -- Training an online logistic regression model using Apache Mahout -- Getting ready -- How to do it... -- Applying an online logistic regression model using Apache Mahout -- Getting ready -- How to do it... -- Solving simple text mining problems with Apache Spark -- Getting ready -- How to do it... -- Clustering using KMeans algorithm with MLib -- Getting ready -- How to do it... -- Creating a linear regression model with MLib -- Getting ready -- How to do it... -- Classifying data points with Random Forest model using MLib -- Getting ready -- How to do it... -- Chapter 8: Learn Deeply from Data -- Introduction -- Creating a Word2vec neural net using Deep Learning for Java (DL4j) -- How to do it... -- How it works... -- There's more -- Creating a Deep Belief neural net using Deep Learning for Java (DL4j) -- How to do it... -- How it works... -- Creating a deep autoencoder using Deep Learning for Java (DL4j) -- How to do it...…”

“…These methods are not only valuable in the fields of environmental sciences, planetology and astrophysics, but also fit into the framework of data processing and the concept of Big Data…”

“…Many experts believe that reliability is not only a matter of statistics but is a multidisciplinary scientific topic, involving materials, tests, simulations, quality tools, manufacturing, electronics, mechatronics, environmental engineering and Big Data, among others. For a complex mechatronic system, failure risks have to be identified at an early stage of the design. …”

Tabla de Contenidos: “…Exercise 2 - creating an Azure Cosmos DB SQL API account -- Adding a new database and a new container -- Adding data to a database -- Querying data -- Exercise 3 - connecting to the Azure Cosmos DB SQL API with the SDK -- Exercise 3 - connecting Azure App Service with Azure Cosmos DB -- Summary -- Questions -- Chapter 10: Big Data Storage Overview -- Exploring Azure Data Lake Storage -- Creating an Azure Data Lake instance using the Azure portal -- Creating an Azure Data Lake instance using the Azure CLI -- Exploring Azure Data Factory -- ADF components -- Creating an ADF using the Azure portal -- Exploring Azure Databricks -- Azure Synapse Analytics -- Azure Databricks features -- Azure Databricks components -- Creating an Azure Databricks workspace -- Exploring Azure Synapse Analytics -- Exploring Azure Analysis Services -- Summary -- Further reading -- Questions -- Part 3: Ensuring Continuous Integration and Continuous Container Deployment on Azure -- Chapter 11: Containers and Continuous Deployment on Azure -- Setting up continuous deployment for Docker with Azure DevOps and Azure Container Registry -- Creating the pipeline -- Continuous deployment for Windows containers with Azure DevOps -- Deploying to Azure Container Registry -- Deploying to Azure App Service -- Integrating Docker Hub with the CI/CD pipeline -- Summary -- Assessments -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…Future Directions -- A Step-by-Step Research Plan -- Open Science Questions -- Open Science Advancing -- Quiz 16.1 -- Archival Data -- Quiz 16.2 -- Dealing With Numerous Dependent Variables -- Big Data -- Quiz 16.3 -- Reporting Your Work -- Take-Home Messages for Chapter 16 -- Take-Home Messages for the Whole Book -- End-of-Chapter Exercises -- Answers to Quizzes -- Answers to In-Chapter Exercises -- Appendix: The esci Software -- Answers to End-of-Chapter Exercises -- Glossary -- References -- Author Index -- Subject Index -- Selected Formulas…”

Tabla de Contenidos: “…-- Tableau and Business Intelligence -- Connecting Big Data with Business Intelligence -- Analyzing Data with Tableau -- Visualizing Data -- Understanding Key Tableau Terms -- Data source -- Data type -- Data fields -- Dimensions and measures -- Continuous versus discrete -- Filter -- Aggregation -- Workbook and worksheet -- Gearing Up for the Tableau Journey -- Understanding installation prerequisites -- Getting familiar with Tableau file types and sources -- Chapter 2 Venturing into Tableau Versions -- Getting to Know the Tableau Product Line -- Tableau Desktop -- Tableau Prep -- Tableau Server and Tableau Cloud -- Tableau Public -- Tableau Reader -- Tableau CRM Analytics -- Tableau Data Management -- Tableau Advanced Management -- Choosing the Right Version -- Chapter 3 Mapping Out the Data Journey -- Cycling Down the Tableau Highway -- Identifying the right question or business need -- Getting raw data -- Choosing mapping techniques -- Viewing and preparing datasets -- Developing visual insights -- Publishing and sharing insights -- Knowing What Tools You Need in Each Stage of the Data Life Cycle -- Understanding User Types and Their Capabilities -- Viewer -- Explorer -- Creator -- Engaging Users Based on User Type -- Access -- Author -- Prepare -- Interact -- Collaborate -- Govern -- Part 2 Prepping Data in Tableau -- Chapter 4 Connecting Your Data -- Understanding Data Source Options -- Connecting to Data -- Making the Desktop or Prep connection -- Locating the Server and Online connections -- Setting Up and Planning the Data Source…”

“…Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial. …”

Tabla de Contenidos: “…NUEVAS NARRATIVAS Y NUEVOS COMUNICADORES -- LA NARRATIVA TRANSMEDIA -- COAUTORÍA Y CONSUMO CONSTRUCTIVO -- EL LENGUAJE DE LA NARRATIVA DIGITAL -- NARRATIVA TRANSMEDIA Y MERCADO -- RELATOS, BIG DATA Y SOFTWARE -- REFLEXIÓN FINAL -- BIBLIOGRAFÍA Y WEBGRAFÍA -- 13. …”

Tabla de Contenidos: “…Fitzek, On Goodput and Energy Measurements of Network Coding Schemes in the Raspberry Pi, Reprinted from: Electronics 2016, 5(4), 66; doi: 10.3390/electronics5040066 -- Wajdi Hajji and Fung Po Tso, Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data, Reprinted from: Electronics 2016, 5(2), 29; doi: 10.3390/electronics5020029 -- Jochen Schlobohm, Andreas Pösch and Eduard Reithmeier, A Raspberry Pi Based Portable Endoscopic 3D Measurement System, Reprinted from: Electronics 2016, 5(3), 43; doi: 10.3390/electronics5030043 -- Vittorio Pasquali, Riccardo Gualtieri, Giuseppe D'Alessandro, Maria Granberg, David Hazlerigg, Marco Cagnetti and Fabio Leccese, Monitoring and Analyzing of Circadian and Ultradian Locomotor Activity, Based on Raspberry-Pi, Reprinted from: Electronics 2016, 5(3), 58; doi: 10.3390/electronics5030058 -- Fabio Leccese, Marco Cagnetti, Stefano Di Pasquale, Sabino Giarnetti and Maurizio Caciotta, A New Power Quality Instrument Based on Raspberry-Pi, Reprinted from: Electronics 2016, 5(4), 64; doi: 10.3390/electronics5040064 -- Ulf Jennehag, Stefan Forsstrom and Federico V. …”

Tabla de Contenidos: “…-- Management -- Azure Data Catalog -- Processing -- Azure Data Factory -- Azure Stream Analytics -- Azure Time Series Insights -- Storage -- Azure Storage Blob service -- Azure Storage Blob service premium -- Azure Storage Queue service -- Azure Storage Table service -- Azure Files -- SQL as a service -- Azure SQL Database -- SQL Server Stretch Database -- Azure PostgreSQL -- Azure MySQL -- Other offers -- NoSQL as a service -- Azure CosmosDB -- Big data -- Azure HDInsight -- Azure Data Lake Store -- Azure Data Lake Analytics -- Analytics -- Cortana Intelligence Suite -- AI -- Azure Machine Learning…”

Tabla de Contenidos: “…5 Least-Squares-Based Linear Spectral Unmixing For Pure Endmembers -- 5.1 Linear Spectral Unmixing -- 5.2 Linear Spectral Unmixing of Endmembers: Case Study of Mangrove Ecosystem -- 5.3 Case Study of Fractional Abundance Estimation of Mangrove Species -- 5.3.1 Endmember Detection -- 5.3.2 Fractional Abundance -- 5.3.3 Accuracy Assessment -- 6 Non-Linear Unmixing for Classification of Mixed Endmembers -- 6.1 Limitations of Linear Spectral Unmixing -- 6.2 Non-Linear Unmixing Models -- 6.3 Nascimento's Bilinear Spectral Unmixing Model -- 6.4 Fan's Bilinear Unmixing Model -- 6.5 Hapke's Bidirectional Model -- 6.6 Higher-Order Non-Linear Spectral Unmixing Models -- 6.7 Experimental Results of Analysis of Lower to Higher-Order Non-Linear Models With Case Studies -- 7 Fuzzy Logic-Based Non-Linear Spectral Unmixing -- 7.1 Fuzzy Logic-Based Non-Linear Spectral Unmixing -- 7.2 Fuzzy C-Means (FCM) -- 7.3 Possibilistic C-Means (PCM) -- 7.4 Entropy-Based Fuzzy C-Means Unmixing -- 7.5 Spatial Fuzzy-Based Unmixing -- 7.6 Applications of Fuzzy-Based Non-Linear Unmixing -- 8 Machine Learning Models for Classification of Hyperspectral Data -- 8.1 Machine Learning Models for Classification of Hyperspectral Data -- 8.2 Classification Models -- 8.3 Prediction Models -- 8.3.1 Deep Learning -- 8.4 Merits and Demerits of the Models Through Experimental Analysis -- 8.5 Role of Machine/Deep Learning in Spatial Big Data Analytics -- Case Study: Evaluating the Performance of a Convolutional Neural Network (CNN) Model in Classifying Hyperspectral Images in the Andaman and Nicobar Islands -- 9 Ecodynamic Modeling -- 9.1 Ecodynamic Modeling -- 9.2 The Ecodynamic Model -- 9.2.1 Ecodynamic Model and Time-Series Analyses of Hyperspectral Data -- 9.2.2 Application of the Ecodynamic Model in Change Detection of Coastal Endmembers…”

Tabla de Contenidos: “…-- Preface xv -- 1 Introduction 1 -- 1.1 Vision: “Big Data” 1 -- 1.2 Cognitive Radio: System Concepts 2 -- 1.3 Spectrum Sensing Interface and Data Structures 2 -- 1.4 Mathematical Machinery 4 -- 1.4.1 Convex Optimization 4 -- 1.4.2 Game Theory 6 -- 1.4.3 “Big Data” Modeled as Large Random Matrices 6 -- 1.5 Sample Covariance Matrix 10 -- 1.6 Large Sample Covariance Matrices of Spiked Population Models 11 -- 1.7 Random Matrices and Noncommutative Random Variables 12 -- 1.8 Principal Component Analysis 13 -- 1.9 Generalized Likelihood Ratio Test (GLRT) 13 -- 1.10 Bregman Divergence for Matrix Nearness 13 -- 2 Spectrum Sensing: Basic Techniques 15 -- 2.1 Challenges 15 -- 2.2 Energy Detection: No Prior Information about Deterministic or Stochastic Signal 15 -- 2.2.1 Detection in White Noise: Lowpass Case 16 -- 2.2.2 Time-Domain Representation of the Decision Statistic 19 -- 2.2.3 Spectral Representation of the Decision Statistic 19 -- 2.2.4 Detection and False Alarm Probabilities over AWGN Channels 20 -- 2.2.5 Expansion of Random Process in Orthonormal Series with Uncorrelated Coefficients: The Karhunen-Loeve Expansion 21 -- 2.3 Spectrum Sensing Exploiting Second-Order Statistics 23 -- 2.3.1 Signal Detection Formulation 23 -- 2.3.2 Wide-Sense Stationary Stochastic Process: Continuous-Time 24 -- 2.3.3 Nonstationary Stochastic Process: Continuous-Time 25 -- 2.3.4 Spectrum Correlation-Based Spectrum Sensing for WSS Stochastic Signal: Heuristic Approach 29 -- 2.3.5 Likelihood Ratio Test of Discrete-Time WSS Stochastic Signal 32 -- 2.3.6 Asymptotic Equivalence between Spectrum Correlation and Likelihood Ratio Test 35 -- 2.3.7 Likelihood Ratio Test of Continuous-Time Stochastic Signals in Noise: Selin's Approach 36 -- 2.4 Statistical Pattern Recognition: Exploiting Prior Information about Signal through Machine Learning 39 -- 2.4.1 Karhunen-Loeve Decomposition for Continuous-Time Stochastic Signal 39 -- 2.5 Feature Template Matching 42 -- 2.6 Cyclostationary Detection 47 -- 3 Classical Detection 51.…”

Tabla de Contenidos: “…References -- EL PROBLEMA DEL SUJETO MORAL EN TIEMPOS DE BIG DATA: ¿HACIA UN NUEVO GIRO COPERNICANO? -- Rosa Colmenarejo Fernández -- 1. …”

Tabla de Contenidos: “…Automated learning and telecommunications networks -- 3.4.4. Big data and telecommunications networks -- 3.4.5. …”

“…Text processing and pattern matching simplified About This Book Master the fastest and most elegant big data munging language Implement text processing and pattern matching using the advanced features of AWK and GAWK Implement debugging and inter-process communication using GAWK Who This Book Is For This book is for developers or analysts who are inclined to learn how to do text processing and data extraction in a Unix-like environment. …”