Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Authors Biography -- Daniel Linstedt -- Michael Olschimke -- Foreword -- Preface -- Acknowledgments -- Daniel Linstedt -- Michael Olschimke -- Chapter 1 - Introduction to Data Warehousing -- 1.1 - History of Data Warehousing -- 1.1.1 - Decision Support Systems -- 1.1.2 - Data Warehouse Systems -- 1.2 - The Enterprise Data Warehouse Environment -- 1.2.1 - Access -- 1.2.2 - Multiple Subject Areas -- 1.2.3 - Single Version of Truth -- 1.2.4 - Single Version of Facts -- 1.2.5 - Mission Criticality -- 1.2.6 - Scalability -- 1.2.7 - Big Data -- 1.2.8 - Performance Issues -- 1.2.9 - Complexity -- 1.2.10 - Auditing and Compliance -- 1.2.11 - Costs -- 1.2.12 - Other Business Requirements -- 1.3 - Introduction to Data Vault 2.0 -- 1.4 - Data Warehouse Architecture -- 1.4.1 - Typical Two-Layer Architecture -- 1.4.2 - Typical Three-Layer Architecture -- References -- Chapter 2 - Scalable Data Warehouse Architecture -- 2.1 - Dimensions of Scalable Data Warehouse Architectures -- 2.1.1 - Workload -- 2.1.2 - Data Complexity -- 2.1.3 - Analytical Complexity -- 2.1.4 - Query Complexity -- 2.1.5 - Availability -- 2.1.6 - Security -- 2.2 - Data Vault 2.0 Architecture -- 2.2.1 - Business Rules Definition -- 2.2.2 - Business Rules Application -- 2.2.3 - Staging Area Layer -- 2.2.4 - Data Warehouse Layer -- 2.2.5 - Information Mart Layer -- 2.2.6 - Metrics Vault -- 2.2.7 - Business Vault -- 2.2.8 - Operational Vault -- 2.2.9 - Managed Self-Service BI -- 2.2.10 - Other Features -- References -- Chapter 3 - The Data Vault 2.0 Methodology -- 3.1 - Project Planning -- 3.1.1 - Capability Maturity Model Integration -- 3.1.1.1 - Capability Levels -- 3.1.1.2 - Maturity Levels -- 3.1.1.3 - Advancing to Maturity Level 5 -- 3.1.1.4 - Integrating CMMI in the Data Vault 2.0 Methodology -- 3.1.2 - Managing the Project…”

“…With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas. …”

Tabla de Contenidos: “…3.3 Conceptual Model of Smart Grid -- 3.4 Building Computerization -- 3.4.1 Smart Lighting -- 3.4.2 Smart Parking -- 3.4.3 Smart Buildings -- 3.4.4 Smart Grid -- 3.4.5 Integration IoT in SG -- 3.5 Challenges and Solutions -- 3.6 Conclusions -- References -- 4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management -- 4.1 Introduction -- 4.1.1 Fundamental Terms in IIoT -- 4.1.1.1 Cloud Computing -- 4.1.1.2 Big Data Analytics -- 4.1.1.3 Fog/Edge Computing -- 4.1.1.4 Internet of Things -- 4.1.1.5 Cyber-Physical-System -- 4.1.1.6 Artificial Intelligence -- 4.1.1.7 Machine Learning -- 4.1.1.8 Machine-to-Machine Communication -- 4.1.2 Intelligent Analytics -- 4.1.3 Predictive Maintenance -- 4.1.4 Disaster Predication and Safety Management -- 4.1.4.1 Natural Disasters -- 4.1.4.2 Disaster Lifecycle -- 4.1.4.3 Disaster Predication -- 4.1.4.4 Safety Management -- 4.1.5 Optimization -- 4.2 Existing Technology and Its Review -- 4.2.1 Survey on Predictive Analysis in Natural Disasters -- 4.2.2 Survey on Safety Management and Recovery -- 4.2.3 Survey on Optimizing Solutions in Natural Disasters -- 4.3 Research Limitation -- 4.3.1 Forward-Looking Strategic Vision (FVS) -- 4.3.2 Availability of Data -- 4.3.3 Load Balancing -- 4.3.4 Energy Saving and Optimization -- 4.3.5 Cost Benefit Analysis -- 4.3.6 Misguidance of Analysis -- 4.4 Finding -- 4.4.1 Data Driven Reasoning -- 4.4.2 Cognitive Ability -- 4.4.3 Edge Intelligence -- 4.4.4 Effect of ML Algorithms and Optimization -- 4.4.5 Security -- 4.5 Conclusion and Future Research -- 4.5.1 Conclusion -- 4.5.2 Future Research -- References -- 5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques -- 5.1 Introduction -- 5.2 Fuzzy Logic -- 5.2.1 Fuzzy Sets -- 5.2.2 Fuzzy Logic Basics -- 5.2.3 Fuzzy Logic and Power System -- 5.2.4 Fuzzy Logic-Automatic Generation Control…”

Tabla de Contenidos: “…9.2.9 Hacking the DNS -- 9.2.10 Hardware Flaws -- 9.2.11 Botnets -- 9.3 Cyber Risk Analysis -- 9.3.1 Kill Chain Approach -- 9.3.2 Machine-learning Approach -- 9.4 Analysis -- 9.5 Discussion -- References -- Chapter 10 Social Hacking -- 10.1 Web 2.0 and the Social Network -- 10.2 Social Networks Amplify Memes -- 10.3 Topology Matters -- 10.4 Computational Propaganda -- 10.5 Beware the Echo Chamber -- 10.6 Big Data Analytics -- 10.6.1 Algorithmic Bias -- 10.6.2 The Depths of Deep Learning -- 10.6.3 Data Brokers -- 10.7 GDPR -- 10.8 Social Network Resilience -- 10.9 The Sustainable Web -- 10.9.1 The Century of Regulation -- 10.9.2 The NetzDG -- 10.10 Discussion -- References -- Chapter 11 Banking and Finance -- CHAPTER MENU -- 11.1 The Financial System -- 11.1.1 Federal Reserve Versus US Treasury -- 11.1.2 Operating the System -- 11.1.3 Balancing the Balance Sheet -- 11.1.4 Paradox of Enrichment -- 11.2 Financial Networks -- 11.2.1 FedWire -- 11.2.2 TARGET -- 11.2.3 SWIFT -- 11.2.4 Credit Card Networks -- 11.2.5 3-D Secure Payment -- 11.3 Virtual Currency -- 11.3.1 Intermediary PayPal -- 11.3.2 ApplePay -- 11.3.3 Cryptocurrency -- 11.3.3.1 Nakamoto's Revenge -- 11.3.3.2 Double Spend Problem -- 11.3.3.3 Crypto Challenges -- 11.4 Hacking a Financial Network -- 11.5 Hot Money -- 11.5.1 Liquidity Traps -- 11.5.2 The Dutch Disease -- 11.6 The End of Stimulus? …”

Tabla de Contenidos: “…2.7 Role of 5G for Transit Operations -- 2.8 Role of 5G in Advanced Driver Assistance Systems (ADAS) -- 2.9 Role of 5G in Logistics Operations -- 2.10 Summary -- References -- Chapter 3 Network Management in Smart Cities -- 3.1 Introduction -- 3.2 Forensics -- 3.2.1 Digital Device Forensics -- 3.2.2 Other Digital Forensics -- 3.2.3 The Need for IoT Forensics -- 3.3 Challenges in IoT Forensics -- 3.3.1 General Issues -- 3.3.2 Evidence Identification, Collection, and Preservation -- 3.3.3 Evidence Analysis and Correlation -- 3.3.4 Presentation -- 3.4 Opportunities of IoT Forensics -- 3.5 Cloud Computing Security -- 3.5.1 Effectively Manage Identities -- 3.5.2 Key Concerns about Cloud Computing -- 3.5.3 Trends in Big Data as an Enabling Technology -- 3.6 Smart Cities -- 3.6.1 Smart City Concept -- 3.6.2 Cloud Computing Benefits in the Context of Smart City -- 3.7 Smarter Grid -- 3.8 Smart Home -- 3.9 Smart City Data Plan Challenges -- 3.9.1 Compatibility between Smart City Devices -- 3.9.2 Simplicity -- 3.9.3 Mobility and Geographic Control -- 3.10 Software-Defined Network-Based Smart City Network Management -- 3.10.1 Centralized Control -- 3.10.2 Simplicity and Inerrability -- 3.10.3 Virtualization -- 3.10.4 Compatibility -- 3.10.5 Challenges of SDN in Smart City Applications -- 3.11 Software-Defined Things Framework -- 3.11.1 Reactive Smart City Device Management -- 3.11.2 Smart Mobility and Smart Traffic Management -- 3.11.3 Smart Environment -- 3.11.4 Security -- 3.11.5 Advanced Optical Network Architecture for Next-Generation Internet Access -- 3.12 Conclusion and Future Work -- References -- Chapter 4 Energy-Efficient Reinforcement Learning in Wireless Sensor Networks Using 5G for Smart Cities -- 4.1 Introduction -- 4.1.1 Problem Statement -- 4.1.2 Objectives -- 4.2 Literature Review -- 4.2.1 Wireless Sensor Network…”

Tabla de Contenidos: “…5.5.1.2 MEC service continuity and mobility and service enhancements -- 5.5.1.3 MEC security and privacy -- 5.5.1.4 Standardization of protocols -- 5.5.1.5 MEC service monetization -- 5.5.1.6 Edge cloud infrastructure and resource management -- 5.5.1.7 Mobile data offloading -- 5.5.2 Application perspective -- 5.5.2.1 Industrial IoT application in 5G -- 5.5.2.2 Large scale healthcare and big data management -- 5.5.2.3 Integration of AI and 5G for MEC enabled healthcare application -- 5.6 Conclusions -- References -- 6 An efficient lightweight speck technique for edge-IoT-based smart healthcare systems -- 6.1 Introduction -- 6.2 The Internet of Things in smart healthcare system -- 6.2.1 Support for diagnosis treatment -- 6.2.2 Management of diseases -- 6.2.3 Risk monitoring and prevention of disease -- 6.2.4 Virtual support -- 6.2.5 Smart healthcare hospitals support -- 6.3 Application of edge computing in smart healthcare system -- 6.4 Application of encryptions algorithm in smart healthcare system -- 6.4.1 Speck encryption -- 6.5 Results and discussion -- 6.6 Conclusions and future research directions -- References -- 7 Deep learning approaches for the cardiovascular disease diagnosis using smartphone -- 7.1 Introduction -- 7.2 Disease diagnosis and treatment -- 7.3 Deep learning approaches for the disease diagnosis and treatment -- 7.3.1 Artificial neural networks -- 7.3.2 Deep learning -- 7.3.3 Convolutional Neural Networks -- 7.4 Case study of a smartphone-based Atrial Fibrillation Detection -- 7.4.1 Smartphone data acquisition -- 7.4.2 Biomedical signal processing -- 7.4.3 Prediction and classification -- 7.4.4 Experimental data -- 7.4.5 Performance evaluation measures -- 7.4.6 Experimental results -- 7.5 Discussion -- 7.6 Conclusion -- References -- 8 Advanced pattern recognition tools for disease diagnosis -- 8.1 Introduction…”

Tabla de Contenidos: “…References -- 10 The Impact of Multicollinearity on Big Data Multivariate Analysis Modeling -- 10.1. Introduction -- 10.2. …”

Tabla de Contenidos: “…Desastres y salud pública -- Definición -- Panorama mundial sobre los desastres -- Ciclo de los desastres -- Fase de preparación -- Fase de alerta -- Fase de impacto -- Fase de emergencia, socorro o asistencia humanitaria -- Fase de rehabilitación y reconstrucción -- Respuesta temprana a un desastre -- Preparación para la respuesta inmediata ante emergencias o desastres -- Acciones del sector salud como respuesta temprana emergencias o desastres -- Abastecimiento de agua y sus buenas condiciones para el consumo -- Fomento de la higiene personal y ambiental -- En el concepto de higiene personal -- En relación con la disposición de excretas -- En cuanto a los desechos sólidos -- Manejo de aguas residuales -- Seguridad alimentaria -- Refugios y asentamientos -- Artículos de uso -- Servicios de salud -- Indicadores -- Bibliografía -- Capítulo 12 .TIC y salud pública -- Introducción -- Conceptos básicos -- Tecnologías de la Información y la Comunicación (TIC) -- eSalud -- Telemedicina -- IoT -- Big data en salud -- Inteligencia artificial en salud -- Contexto de las tecnologías de la información y la comunicación (TIC) en la salud pública -- Tecnologías de la información y la comunicación (TIC) en las competencias de la salud pública -- Tecnologías de la información y la comunicación (TIC) en la promoción, información, educación y form -- Tecnologías de la información y la comunicación (TIC) en el monitoreo y seguimiento del tratamiento -- Tecnologías emergentes en apoyo a la salud pública -- Monitoreo continuo -- Supermercado de la salud -- Hogar conectado -- Acceso automático del paciente -- Círculos del cuidado vital…”

Tabla de Contenidos: “…The Customer Doesn't Own the Service Asset -- The Digital Transformation of Services Branding and Marketing -- The Rise of Self-Service -- New Opportunities through Informed Contact -- From Product Manufacturing to Service Providers -- Building a Powerful Services Brand -- Be Clear on the Value Proposition and How to Execute It -- Create Standards -- Engage Employees and Communicate, Communicate, Communicate -- Use Metrics to Track Progress -- Consider Loyalty Programs -- Summary -- Notes -- SECTION THREE Gaining Insight about Your Brand and Quantifying Its Statur -- Chapter 13 Digital Transformation and the Evolution of Customer Insights in Brand Building -- The Value of the Customer Journey Mindset -- Exploratory Empathy-Driven Insights and Innovation -- Persona Development -- Measurement and Data Collection Opportunities -- Elevating Research Methods to Leverage Customer Journey Insights -- Qualitative Methods: Relentless Pursuit of the "Why" -- Quantitative Methods: Harnessing the Power of Big Tech/Big Data -- In Practice: How to Ensure that Modern Insights Can Deliver for the Brand -- Summary -- Notes -- Chapter 14 Using Neuroscience to Assess Brands -- The Neuroscience Tool Kit -- EEG -- fMRI -- Eye-Tracking -- Emerging Tools -- Neuroscience and Brand Insight -- fMRI Imaging and Brand Associations -- EEG, Brand Emotions, and Engagement -- fMRI, EEG, and Brand Design -- Summary -- Notes -- Chapter 15 Measuring Brand Relevance and Health -- Brand Measurement Techniques -- Awareness Tracking -- Attitudinal Brand Tracking -- Asking Open-Ended Questions: A Less-Biased Way To Measure Brand Associations -- Assessing Brand Health and Defining Brand-Building Strategies -- Lego (High-High-High): The Case for Brand Extension -- Samsung (High-Low-Low): The Case for Rebranding -- Barilla: (Low-High-Low): The Case for Awareness Building…”

Tabla de Contenidos: “…. -- net sockets are streams -- Unix sockets -- UDP sockets -- See also -- Chapter 4: Using Streams -- Introduction -- Processing Big Data -- Getting ready -- How to do it... -- How it works... -- There's more... -- Types of stream -- Processing infinite amounts of data -- Flow mode versus pull-based streaming -- Understanding stream events -- See also -- Using the pipe method -- Getting ready -- How to do it... -- How it works... -- There's more... -- Keeping piped streams alive -- See also -- Piping streams in production -- Getting ready -- How to do it... -- How it works... -- There's more... -- Use pumpify to expose pipelines -- See also -- Creating transform streams -- Getting ready -- How to do it... -- How it works... -- There's more... -- Transform streams with Node's core stream module -- Creating object mode transform streams -- See also -- Creating readable and writable streams -- Getting ready -- How to do it... -- How it works…”

Tabla de Contenidos: “…. -- Systems for Different Management Groups -- Spotlight On: Organizations: Carbon Lighthouse Lights Up with the Internet of Things (IoT), Big Data, and Cloud Computing -- Systems for Linking the Enterprise -- E-business, E-commerce, and E-government -- 2-3 Understand why systems for collaboration, social business, and knowledge management are so important and the technologies they use. -- What is Collaboration? …”

Tabla de Contenidos: “…A survey of NHS e-therapy developers -- Matthew Russell Bennion, Gillian E Hardy, Roger K Moore, Stephen Kellett, Abigail Millings -- Nurses' perceptions of the clinical information system in primary healthcare centres in Qatar: a cross-sectional survey -- Monaa Hussain Mansoori, Kathleen Benjamin, Emmanuel Ngwakongnwi, Samya Al Abdulla -- Phishing in healthcare organisations: threats, mitigation and approaches -- Ward Priestman, Tony Anstis, Isabel G Sebire, Shankar Sridharan, Neil J Sebire -- LAGOS: learning health systems and how they can integrate with patient care -- Scott McLachlan, Kudakwashe Dube, Evangelia Kyrimi, Norman Fenton -- Developing and sustaining digital professionalism: a model for assessing readiness of healthcare environments and capability of nurses -- Carey Ann Mather, Elizabeth Cummings -- Measuring the outcomes of using person-generated health data: a case study of developing a PROM item bank -- Gerardo Luis Dimaguila, Kathleen Gray, Mark Merolli -- Evaluating the quality of voice assistants' responses to consumer health questions about vaccines: an exploratory comparison of Alexa, Google Assistant and Siri -- Emily Couvillon Alagha, Rachel Renee Helbing -- Patient and carer survey of remote vital sign telemonitoring for self-management of long-term conditions -- Julie-Ann Walkden, Paul Joseph McCullagh, W George Kernohan -- Physician leadership and health information exchange: literature review -- Michele L Heath, Tracy H Porter -- Evaluating a post-implementation electronic medical record training intervention for diabetes management in primary care -- Gurprit Kaur Randhawa, Aviv Shachak, Karen L Courtney, Andre Kushniruk -- Using normalisation process theory to understand workflow implications of decision support implementation across diverse primary care settings -- Rebecca G Mishuris, Joseph Palmisano, Lauren McCullagh, Rachel Hess, David A Feldstein, Paul D Smith, Thomas McGinn, Devin M Mann -- Improved efficiency and patient safety through bespoke electronic thalassaemia care module -- Mohamed Naveed, Yousif Al-Serkal, Sumaya Al-Nuaimi, Kalthoom Al-Blooshi, Noor Majed Al-Mahiri, Yasir Khan, Sadaf Ahsan Naqvi, Neema Preman -- Reviews -- Cerebral palsy information system with an approach to information architecture: a systematic review -- Mina Afzali, Korosh Etemad, Alireza Kazemi, Reza Rabiei -- mHealth and big-data integration: promises for healthcare system in India -- Samaneh Madanian, Dave T Parry, David Airehrour, Marianne Cherrington -- Reliability of administrative data to identify sexually transmitted infections for population health: a systematic review -- Brian E Dixon, Saurabh Rahurkar, Yenling Ho, Janet N Arno -- Short Reports -- Short-term adoption rates for a web-based portal within the intranet of a hospital information system -- Athanasios Kotoulas, Ioannis Stratis, Theodoros Goumenidis, George Lambrou,…”

Tabla de Contenidos: “…-- How to Become Certified -- Who Should Buy This Book -- How This Book Is Organized -- Chapter Features -- Bonus Digital Contents -- Conventions Used in This Book -- Google Cloud Professional ML Engineer Objective Map -- How to Contact the Publisher -- Chapter 1 Framing ML Problems -- Translating Business Use Cases -- Machine Learning Approaches -- Supervised, Unsupervised, and Semi-supervised Learning -- Classification, Regression, Forecasting, and Clustering -- ML Success Metrics -- Regression -- Responsible AI Practices -- Summary -- Exam Essentials -- Review Questions -- Chapter 2 Exploring Data and Building Data Pipelines -- Visualization -- Box Plot -- Line Plot -- Bar Plot -- Scatterplot -- Statistics Fundamentals -- Mean -- Median -- Mode -- Outlier Detection -- Standard Deviation -- Correlation -- Data Quality and Reliability -- Data Skew -- Data Cleaning -- Scaling -- Log Scaling -- Z-score -- Clipping -- Handling Outliers -- Establishing Data Constraints -- Exploration and Validation at Big-Data Scale -- Running TFDV on Google Cloud Platform -- Organizing and Optimizing Training Datasets -- Imbalanced Data -- Data Splitting -- Data Splitting Strategy for Online Systems -- Handling Missing Data -- Data Leakage -- Summary -- Exam Essentials -- Review Questions -- Chapter 3 Feature Engineering -- Consistent Data Preprocessing -- Encoding Structured Data Types -- Mapping Numeric Values -- Mapping Categorical Values -- Feature Selection -- Class Imbalance -- Classification Threshold with Precision and Recall -- Area under the Curve (AUC)…”

Tabla de Contenidos: “…Intro -- inside front cover -- Privacy-Preserving Machine Learning -- Copyright -- contents -- front matter -- preface -- acknowledgments -- about this book -- Who should read this book -- How this book is organized: A road map -- About the code -- liveBook discussion forum -- about the authors -- about the cover illustration -- Part 1 Basics of privacy-preserving machine learning with differential privacy -- 1 Privacy considerations in machine learning -- 1.1 Privacy complications in the AI era -- 1.2 The threat of learning beyond the intended purpose -- 1.2.1 Use of private data on the fly -- 1.2.2 How data is processed inside ML algorithms -- 1.2.3 Why privacy protection in ML is important -- 1.2.4 Regulatory requirements and the utility vs. privacy tradeoff -- 1.3 Threats and attacks for ML systems -- 1.3.1 The problem of private data in the clear -- 1.3.2 Reconstruction attacks -- 1.3.3 Model inversion attacks -- 1.3.4 Membership inference attacks -- 1.3.5 De-anonymization or re-identification attacks -- 1.3.6 Challenges of privacy protection in big data analytics -- 1.4 Securing privacy while learning from data: Privacy-preserving machine learning -- 1.4.1 Use of differential privacy -- 1.4.2 Local differential privacy -- 1.4.3 Privacy-preserving synthetic data generation -- 1.4.4 Privacy-preserving data mining techniques -- 1.4.5 Compressive privacy -- 1.5 How is this book structured? …”

Tabla de Contenidos: “…Training and Development -- Career Development -- Developing Leaders -- Functional Area 5: Total Rewards -- Key Concepts -- Total Rewards and Organizational Strategy -- Compensation Structure -- Compensation Systems -- Benefits and Perquisites -- Legislation Affecting Compensation and Benefits -- Chapter Review -- Questions -- Answers -- References -- Chapter 5 Organization -- Functional Area 6: Structure of the HR Function -- Key Concepts -- The Strategic Role of HR -- Understanding the Organization -- The HR Organization and Function -- Measuring and Demonstrating HR Value -- HR's Role in Organizational Strategy -- Functional Area 7: Organizational Effectiveness &amp -- Development -- Key Concepts -- Overview of Organizational Effectiveness and Development (OED) -- Organizational Gap Development -- Implementing OED Initiatives -- Measuring Organizational Effectiveness and Development -- Functional Area 8: Workforce Management -- Key Concepts -- Organizational Workforce Requirements -- Workforce Planning -- The Staffing Plan -- Employee Development -- Succession Planning -- Knowledge Management -- Functional Area 9: Employee &amp -- Labor Relations -- Key Concepts -- The Employment Relationship -- Third-Party Influences on Employee Relations -- When the Employee Relationship Falters -- Functional Area 10: Technology Management -- Key Concepts -- HR and Technology -- HR in the Era of Big Data -- HR Information Systems -- Policies for Technology Use in the Workplace -- Chapter Review -- Questions -- Answers -- References -- Chapter 6 Workplace -- Functional Area 11: Managing a Global Workforce -- Key Concepts -- The Global Context -- Defining the Global Organization -- Creating a Global Strategy -- Becoming a Multicultural Organization -- Managing Global Assignments -- Navigating the Global Legal Environment -- Functional Area 12: Risk Management…”

Tabla de Contenidos: “….: DESIGNING EXECUTIVE DASHBOARDS -- Introduction -- Dashboard Design Goals -- Defining Key Performance Indicators -- Defining Supporting Analytics -- Choosing the Correct KPI Visualization Components -- Supporting Analytics -- A Word about Labeling Your Charts and Graphs -- Putting It All Together: Using Size, Contrast, and Position -- Validating Your Design -- 6.14 ALL THAT GLITTERS IS NOT GOLD -- 6.15 USING EMOTICONS -- 6.16 MISLEADING INDICATORS -- 6.17 AGILE AND SCRUM METRICS -- Introduction: Agile Overview -- Agile Metrics -- General Agile Metrics -- Scrum Metrics -- Other Sprint Charts -- Iteration Metrics -- Scaled Agile Metrics -- Lean Kanban Metrics -- Summary -- 6.18 DATA WAREHOUSES -- The Growth of Business Intelligence Systems -- Big Data -- 6.19 DASHBOARD DESIGN TIPS -- Colors -- Fonts and Font Size -- Use Screen Real Estate -- Component Placement -- 6.20 TEAMQUEST CORPORATION -- White Paper #1: Metric Dashboard Design -- White Paper #2: Proactive Metrics Management -- The Future -- Conclusion -- 6.21 A SIMPLE TEMPLATE -- 6.22 SUMMARY OF DASHBOARD DESIGN REQUIREMENTS -- The Importance of Design to Information Dashboards…”

Tabla de Contenidos: “…The SFSN and the systems context -- Relationship among rescheduling, stability, and nervousness -- Time-related features -- Inner system issues that leverage nervousness -- Outer system nervousness management mechanisms -- A simple SFSN conceptual model -- Physical dimension -- Temporal dimension -- Wrapping it up -- The framework in practice: an illustrative case -- 12.6 Conclusions -- Acknowledgments -- References -- 13 Digital and smart production planning and control -- 13.1 Production planning and control evolution -- 13.1.1 Production planning and control 1.0 (until 1960s) -- 13.1.2 Production planning and control 2.0 (between 1970s and 1980s) -- 13.1.3 Production planning and control 3.0 - (between 1990s and 2010s) -- 13.1.4 Production planning and control 4.0 - (from 2010s) -- 13.2 A bibliometric analysis on digital and smart production planning and control -- 13.3 Digital and smart production planning and control frameworks -- 13.3.1 Framework of classical PPC updated by digital technologies -- 13.3.2 Framework of production planning and control as a service (PPCaaS) -- 13.4 Digital technologies applied in the production planning and control -- 13.4.1 Additive manufacturing (AM) -- 13.4.2 Big data analytics (BDA) -- 13.4.3 Digital twin (DT) -- 13.4.4 Machine learning (ML) -- 13.5 The future of Production Planning and Control 4.0 concept -- References -- 14 Simulation-based generation of rescheduling knowledge using a cognitive architecture -- 14.1 Introduction -- 14.2 Conceptual modeling -- 14.3 Problem-Space Computational Model (PSCM) -- 14.4 Representation and design of schedule states and repair operators -- 14.4.1 Design of repair operators proposition-evaluation, decision, and application knowledge -- 14.4.1.1 Design and implementation of operators proposition-evaluation knowledge (Kpe)…”

Tabla de Contenidos: “…Incidents by date -- Incidents in a polygon -- Buffers -- Nearest neighbor -- Interactive widgets -- Charts -- Triggers -- Summary -- Chapter 8: Automating QGIS Analysis -- Working in the Python console -- Loading layers -- Processing a layer -- Layer properties -- Feature properties -- Drawing a layer from PostGIS -- Drawing points -- Drawing polygons from PostGIS -- Adding, editing, and deleting features -- Adding features to an existing layer -- Deleting features from an existing layer -- Editing features from an existing layer -- Selecting features using expressions -- Using toolboxes in Python -- Writing custom toolboxes -- Summary -- Chapter 9: ArcGIS API for Python and ArcGIS Online -- Introducing the ArcGIS API for Python and ArcGIS Online -- A Pythonic web API -- Installing the API -- Testing the API -- Troubleshooting -- Authenticating your Esri user accounts -- Different Esri user accounts -- Different modules of the ArcGIS API for Python -- Exercise 1 - importing the API and using the map widget -- Creating a personalized ArcGIS Online account -- Exercise 2 - searching, displaying, and describing geospatial content -- Exercise 3 - working with raster data and the API's geoprocessing functions -- Summary -- Chapter 10: Geoprocessing with a GPU Database -- Cloud geodatabase solutions -- Big data processing -- MapD architecture -- Cloud versus local versus combined -- Creating a MapD instance in the cloud -- Finding the AMI -- Opening an AWS account -- Creating a key pair -- Launching an instance -- Picking a version -- Searching for an instance -- Setting up a security group -- Immerse environment -- Logging in to Immerse -- Default dashboards -- NYC taxi dataset -- Importing a CSV -- Creating a chart -- Selections with the SQL EDITOR -- Use geospatial data -- Connecting to the database using a terminal -- PuTTYgen…”

Tabla de Contenidos: “…Chapter 5 A Review of Innovation to Human Augmentation in Brain-Machine Interface - Potential, Limitation, and Incorporation of AI -- 5.1 Introduction -- 5.2 Technologies in Neuroscience for Recording and Influencing Brain Activity -- 5.2.1 Brain Activity Recording Technologies -- 5.2.1.1 A Non-Invasive Recording Methodology -- 5.2.1.2 An Invasive Recording Methodology -- 5.3 Neuroscience Technology Applications for Human Augmentation -- 5.3.1 Need for BMI -- 5.3.1.1 Need of BMI Individuals for Re-Establishing the Control and Communication of Motor -- 5.3.1.2 Brain-Computer Interface Noninvasive Research at Wadsworth Center -- 5.3.1.3 An Interface of Berlin Brain-Computer: Machine Learning-Dependent of User-Specific Brain States Detection -- 5.4 History of BMI -- 5.5 BMI Interpretation of Machine Learning Integration -- 5.6 Beyond Current Existing Methodologies: Nanomachine Learning BMI Supported -- 5.7 Challenges and Open Issues -- 5.8 Conclusion -- References -- Chapter 6 Resting-State fMRI: Large Data Analysis in Neuroimaging -- 6.1 Introduction -- 6.1.1 Principles of Functional Magnetic Resonance Imaging (fMRI) -- 6.1.2 Resting State fMRI (rsfMRI) for Neuroimaging -- 6.1.3 The Measurement of Fully Connected and Construction of Default Mode Network (DMN) -- 6.2 Brain Connectivity -- 6.2.1 Anatomical Connectivity -- 6.2.2 Functional Connectivity -- 6.3 Better Image Availability -- 6.3.1 Large Data Analysis in Neuroimaging -- 6.3.2 Big Data rfMRI Challenges -- 6.3.3 Large rfMRI Data Software Packages -- 6.4 Informatics Infrastructure and Analytical Analysis -- 6.5 Need of Resting-State MRI -- 6.5.1 Cerebral Energetics -- 6.5.2 Signal to Noise Ratio (SNR) -- 6.5.3 Multi-Purpose Data Sets -- 6.5.4 Expanded Patient Populations -- 6.5.5 Reliability -- 6.6 Technical Development -- 6.7 rsfMRI Clinical Applications…”

Tabla de Contenidos: “…Intro -- Statistical Intervals -- Contents -- Preface to Second Edition -- Overview -- Elaboration on New Methods -- New Technical Appendices -- Computer Software -- More on Book's Webpage -- Summary of Changes from First Edition -- Preface to First Edition -- Acknowledgments -- About the Companion Website -- Chapter 1 Introduction, Basic Concepts, and Assumptions -- Objectives and Overview -- 1.1 Statistical Inference -- 1.2 Different Types of Statistical Intervals: An Overview -- 1.3 The Assumption of Sample Data -- 1.4 The Central Role of Practical Assumptions Concerning Representative Data -- 1.5 Enumerative versus Analytic Studies -- 1.5.1 Differentiating between Enumerative and Analytic Studies -- 1.5.2 Statistical Inference for Analytic Studies -- 1.5.3 Inferential versus Predictive Analyses -- 1.6 Basic Assumptions for Inferences from Enumerative Studies -- 1.6.1 Definition of the Target Population and Frame -- 1.6.2 The Assumption of a Random Sample -- 1.6.3 More Complicated Random Sampling Schemes -- 1.7 Considerations in the Conduct of Analytic Studies -- 1.7.1 Analytic Studies -- 1.7.2 The Concept of Statistical Control -- 1.7.3 Other Analytic Studies -- 1.7.4 How to Proceed -- 1.7.5 Planning and Conducting an Analytic Study -- 1.8 Convenience and Judgment Samples -- 1.9 Sampling People -- 1.10 Infinite Population Assumptions -- 1.11 Practical Assumptions: Overview -- 1.12 Practical Assumptions: Further Example -- 1.13 Planning the Study -- 1.14 The Role of Statistical Distributions -- 1.15 The Interpretation of Statistical Intervals -- 1.16 Statistical Intervals and Big Data -- 1.17 Comment Concerning Subsequent Discussion -- BIBLIOGRAPHIC NOTES -- Chapter 2 Overview of Different Types of Statistical Intervals -- Objectives and Overview -- 2.1 Choice of a Statistical Interval -- 2.1.1 Purpose of the Interval…”