“…Over 110 effective recipes to help you build and operate OpenStack cloud computing, storage, networking, and automation About This Book Explore many new features of OpenStack's Juno and Kilo releases Install, configure, and administer core projects with the help of OpenStack Object Storage, Block Storage, and Neutron Networking services Harness the abilities of experienced OpenStack administrators and architects, and run your own private cloud successfully Practical, real-world examples of each service and an accompanying Vagrant environment that helps you learn quickly In Detail OpenStack Open Source software is one of the most used cloud infrastructures to support software development and big data analysis. It is developed by a thriving community of individual developers from around the globe and backed by most of the leading players in the cloud space today. …”

“…These have many applications including game development, big data analytics, financial engineering and analysis, enterprise applications and more. …”

“…Excel PowerPivot & Power Query For Dummies shows you how this powerful new set of tools can be leveraged to more effectively source and incorporate 'big data' Business Intelligence and Dashboard reports. …”

Tabla de Contenidos: “…Innovation and Marketing -- Product Life Cycle (PLC) and Consumer Behaviour -- Summary -- End of Chapter Discussion Questions -- Questions -- References -- Chapter 12 Contemporary Consumer Research -- Learning Outcome -- Introduction -- The Need for Consumer Research -- Setting the Stage for Consumer Research -- Consumer Research Brief -- Consumer Research Proposal -- Perspectives and Paradigms On Consumer Research -- The Marketing Research Process in a Digital Age -- Definition of Problem and Research Objectives -- Formulation of Research Design -- Data Sources -- Secondary Data -- Primary Data -- Survey -- Experimentation -- Qualitative Vs Quantitative Consumer Research Data -- In-depth Interview -- Ethnography -- Focus Group Discussion -- Sampling Plan -- Research Instrument -- Data Collection -- Data Analysis -- Quantitative Analysis -- Qualitative Data Analysis -- Presentation of Findings -- Netnography in Consumer Research -- Big Data in Contemporary Marketing Research: An Overview -- Neuromarketing and the Contemporary Consumer Research -- Ethics and Consumer Research -- Summary -- End of Chapter Discussion Questions -- Questions -- References -- Chapter 13 Consumer Behaviour and Technology: A Look Into the Future -- Learning Outcome -- Introduction -- The Future of Technology and Consumer Behaviour -- Service Robots -- Wearable Technology -- Artificial Intelligence and Marketing -- STP and Consumer Demographics -- Consumer Persona -- Webographics -- Customer Trust and Loyalty -- Summary -- End of Chapter Discussion Questions -- Questions -- References -- Index…”

“…Nearly every large corporation and governmental agency is taking a fresh look at their current enterprise-scale business intelligence (BI) and data warehousing implementations at the dawn of the ""Big Data Era""...and most see a critical need to revitalize their current capabilities. …”

Tabla de Contenidos: “…Cover -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introducing Google Cloud AI Services -- Google Cloud Platform -- Compute -- Big data -- Identity and security -- Internet of Things (IoT) -- Storage and databases -- Data transfer -- API platform and ecosystem -- Management tools -- Networking -- Cloud AI -- Developer tools -- Cognition on cloud -- Clients -- Data types -- Cognitive services -- Why Cognition on Cloud? …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: A Gentle Introduction to Machine Learning -- Introduction - classic and adaptive machines -- Descriptive analysis -- Predictive analysis -- Only learning matters -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Computational neuroscience -- Beyond machine learning - deep learning and bio-inspired adaptive systems -- Machine learning and big data -- Summary -- Chapter 2: Important Elements in Machine Learning -- Data formats -- Multiclass strategies -- One-vs-all -- One-vs-one -- Learnability -- Underfitting and overfitting -- Error measures and cost functions -- PAC learning -- Introduction to statistical learning concepts -- MAP learning -- Maximum likelihood learning -- Class balancing -- Resampling with replacement -- SMOTE resampling -- Elements of information theory -- Entropy -- Cross-entropy and mutual information -- Divergence measures between two probability distributions -- Summary -- Chapter 3: Feature Selection and Feature Engineering -- scikit-learn toy datasets -- Creating training and test sets -- Managing categorical data -- Managing missing features -- Data scaling and normalization -- Whitening -- Feature selection and filtering -- Principal Component Analysis -- Non-Negative Matrix Factorization -- Sparse PCA -- Kernel PCA -- Independent Component Analysis -- Atom extraction and dictionary learning -- Visualizing high-dimensional datasets using t-SNE -- Summary -- Chapter 4: Regression Algorithms -- Linear models for regression -- A bidimensional example -- Linear regression with scikit-learn and higher dimensionality -- R2 score -- Explained variance -- Regressor analytic expression -- Ridge, Lasso, and ElasticNet -- Ridge -- Lasso…”

Tabla de Contenidos: “…-- Entry #4 How can you use models in a meaningful way for your market? -- Entry #5 Does "big data" have the right customer satisfaction answers? …”

“…The world of work is constantly changing, and staying professionally relevant while competing for more specialized tech jobs in areas like cloud computing, mobile and social applications, and big data in a highly competitive global economy is critical. …”

“…Spring enterprise: Spring Java EE integration, Spring Integration, Spring Batch, Spring Remoting, messaging, transactions, and working with big data and the cloud using Hadoop and MongoDB. Spring web: Spring MVC, other dynamic scripting, integration with the popular Grails Framework (and Groovy), REST/web services, and more This book guides you step-by-step through topics using complete and real-world code examples. …”

“…There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. …”

Tabla de Contenidos: “…Data Transmission (EDI and XML) -- Databases and Business Analytics -- Application Systems -- Enterprise Resource Planning (ERP) -- Procurement Systems -- Advanced Planning and Scheduling -- Transportation Planning Systems -- Demand Planning -- Customer Relationship Management (CRM) and Sales Force Automation (SFA) -- Supply Chain Management (SCM) -- Inventory Management Systems -- Manufacturing Execution Systems (MES) -- Transportation Scheduling Systems -- Warehouse Management Systems (WMS) -- New Supply Chain Technology -- Industrial Robots -- Drones and Driverless Vehicles -- Artificial Intelligence (AI) -- Internet of Things (IoT) and Big Data -- Real-Time Product Information -- 3D Printing and Additive Manufacturing -- Simulation Modeling -- Impact on Supply Chain Operations -- Combining New Technologies for Agile Supply Chains -- Putting New Technologies to Work -- Increasing Productivity and Customer Service -- Chapter Summary -- Chapter 5 Metrics for Measuring Supply Chain Performance -- Useful Model of Markets and Their Supply Chains -- Market Performance Categories -- Customer Service -- Internal Efficiency -- Product Demand Flexibility -- Product Development -- A Framework for Performance Measurement -- Customer Service Metrics -- Build to Stock -- Build to Order -- Internal Efficiency Metrics -- Inventory Value -- Inventory Turns -- Return on Sales -- Cash-to-Cash Cycle Time -- Demand Flexibility Metrics -- Activity Cycle Time -- Upside Flexibility -- Outside Flexibility -- Product Development Metrics -- Operations that Enable Supply Chain Performance -- Collecting and Displaying Performance Data -- Three Levels of Detail -- The Data Warehouse -- Spotlighting Problems and Finding Opportunities -- Markets Migrate from One Quadrant to Another -- Sharing Data across the Supply Chain -- Chapter Summary…”

Tabla de Contenidos: “…Cover; Titel; Impressum; Inhaltsverzeichnis; 1 Einführung in Data-Warehouse-Systeme; 1.1 Anwendungsszenario Getränkemarkt; 1.2 OLTP versus OLAP; 1.2.1 OLAP- versus OLTP-Transaktionen; 1.2.2 Vergleich von OLTP und OLAP; 1.2.3 Abgrenzung: DBMS-Techniken; 1.3 Charakteristika und Begriffe; 1.4 Big Data und Data Warehousing; 1.5 Aufbau des Buches; 1.6 Vertiefende Literatur; 1.7 Übungen; 2 Architektur; 2.1 Anforderungen; 2.1.1 Grobe Übersicht über Data-Warehouse-Systeme; 2.1.2 Anforderungen an die Architektur; 2.1.3 Die 12 OLAP-Regeln nach Codd; 2.1.4 Die FASMI-Anforderungen…”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Book 1 Defining Data Science -- Chapter 1 Considering the History and Uses of Data Science -- Considering the Elements of Data Science -- Considering the emergence of data science -- Outlining the core competencies of a data scientist -- Linking data science, big data, and AI -- Understanding the role of programming -- Defining the Role of Data in the World -- Enticing people to buy products -- Keeping people safer -- Creating new technologies -- Performing analysis for research -- Providing art and entertainment -- Making life more interesting in other ways -- Creating the Data Science Pipeline -- Preparing the data -- Performing exploratory data analysis -- Learning from data -- Visualizing -- Obtaining insights and data products -- Comparing Different Languages Used for Data Science -- Obtaining an overview of data science languages -- Defining the pros and cons of using Python -- Defining the pros and cons of using R -- Learning to Perform Data Science Tasks Fast -- Loading data -- Training a model -- Viewing a result -- Chapter 2 Placing Data Science within the Realm of AI -- Seeing the Data to Data Science Relationship -- Considering the data architecture -- Acquiring data from various sources -- Performing data analysis -- Archiving the data -- Defining the Levels of AI -- Beginning with AI -- Advancing to machine learning -- Getting detailed with deep learning -- Creating a Pipeline from Data to AI -- Considering the desired output -- Defining a data architecture -- Combining various data sources -- Checking for errors and fixing them -- Performing the analysis -- Validating the result -- Enhancing application performance…”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Data Science and Python -- Chapter 1 Discovering the Match between Data Science and Python -- Defining the Sexiest Job of the 21st Century -- Considering the emergence of data science -- Outlining the core competencies of a data scientist -- Linking data science, big data, and AI -- Understanding the role of programming -- Creating the Data Science Pipeline -- Preparing the data -- Performing exploratory data analysis -- Learning from data -- Visualizing -- Obtaining insights and data products -- Understanding Python's Role in Data Science -- Considering the shifting profile of data scientists -- Working with a multipurpose, simple, and efficient language -- Learning to Use Python Fast -- Loading data -- Training a model -- Viewing a result -- Chapter 2 Introducing Python's Capabilities and Wonders -- Why Python? …”

Tabla de Contenidos: “…Machine generated contents note: Contents Forward Preface 1 -- INTRODUCTIONS AND MOTIVATION 1.1 Introduction 1.2 The book 1.2.1 Objectives 1.2.2 Benefits 1.2.3 Organization 1.2.4 Book Cover 1.2.5 Impact of C-IoT 1.2.6 Summary 1.3 C-IoT Terms of References 1.3.1 Introduction 1.3.2 Need for IoT Framework 1.3.3 C-IoT Domains and Business Apps Model 1.3.4 C-IoT Roadmap 1.3.5 C-IoT Platform and Developer Community 1.3.6 C-IoT Opportunities for Business apps, solutions and systems 1.4 The Future 1.4.1 General Trends 1.4.2 Point Solutions 1.4.3 Collaborative IoT 1.4.4 C-IoT and RFID 1.4.5 C-IoT and Nanotechnology 1.4.6 Cyber-Collaborative IoT (C2-IoT) 1.4.7 C2-IoT and EBOLA Case 1.4.8 Summary 2 -- APPLICATION REQUIREMENTS 2.1 C-IOT Landscape 2.1.1 C-IoT Model and Architecture Layers 2.1.2 C-IoT Model and Enabling Technologies 2.1.3 Definition of key elements 2.1.4 Requirement Considerations 2.1.5 C-IoT System Solution - Requirement Considerations 2.2 Applications Requirement - Use Cases 2.3 Health & Fitness (Lead Example) 2.3.1 Landscape 2.3.2 Health & Fitness - Sensing Requirements 2.3.3 Health & Fitness - Gateway Requirements 2.3.4 Health & Fitness - Service Requirements 2.3.5 Health & Fitness - Solution Considerations 2.3.6 Health & Fitness - System Considerations 2.3.7 Health & Fitness and Hospitals 2.4 Video Surveillance 2.4.1 Landscape 2.4.2 Video Surveillance - Across Home, Industry and Infrastructure 2.4.3 Video Surveillance - Sensing Requirements 2.4.4 Video Surveillance - Gateway Requirements 2.4.5 Video Surveillance - Services 2.4.6 Example: Red Light Camera - Photo Enforcement Camera 2.4.7 Conclusion 2.5 Smart Home & Building 2.5.1 Landscape 2.5.2 Requirement 2.5.3 Home - Sensing Requirements 2.5.4 Home - Gateway Requirements 2.5.5 Home - Services 2.6 Smart Energy 2.6.1 Landscape 2.6.2 Requirements 2.6.3 Smart Energy - Sensing Requirements 2.6.4 Smart Energy - Gateway Requirements 2.6.5 Smart Energy - Services 2.6.6 The Smart Energy App 2.6.7 Smart Energy and Network Security 2.7 Track & Monitor 2.7.1 Landscape 2.7.2 Track & Monitory - Sensing Requirements 2.7.3 Track & Monitor - Services 2.7.4 Track & Monitor - Solution Considerations 2.7.5 Track & Monitor - Examples 2.8 Smart Factory/Manufacturing 2.8.1 Factory Automation - Robot 2.8.2 Caregiver and Robot 2.8.3 Industrial Robot 2.9 Others: Smart Car, Smart Truck and Smart City 2.9.1 Smart Car 2.9.2 Smart Roadside 2.9.3 Drone 2.9.4 Machine Vision 2.9.5 Smart City 3 -- C-IOT APPLICATIONS AND SERVICES 3.1 Smart IoT Application Use Cases 3.1.1 Health monitoring - Individual level (Fitness/Health Tracking wearables) 3.1.2 Health Monitoring at Business level (used in clinic) 3.1.3 Home and Building Automation - Individual level (Smart Home) 3.1.3.1 Smart Thermostat (Smart Energy Management) 3.1.3.2 Smart Smoke Alarm (Safety) 3.1.3.3 Smart IP Camera for Video Surveillance (Security) 3.1.3.4 Smart Service Robots at Consumer level - Roombas iRobot 3.1.3.5 Smart Home Gateway (Scalable for Smart Building Automation) 3.1.3.6 Smart Building Automation 3.1.4 Smart Energy and Smart Grid 3.1.5 Smart Energy Gateways 3.1.6 Industrial and Factory Automation 3.1.7 Smart Transportation & Fleet Logistics (Connected Cars - V2X: V2V, V2I) 3.1.8 Smart City 3.2 Smart IoT Platform 3.2.2 Smart IoT Software Gateway Platform 3.2.3 Smart Sensor Fusion Platform 3.3 Secured C-IoT Software Platform 3.3.1 C-IoT Security - Example on Smart Energy 3.3.2 Securing NAN (Metrology-to-Concentrator) 3.3.3 Securing Home Area Network (HAN) 3.3.4 Securing WAN (Concentrator-to-Sub Station/Utility Servers) 3.3.5 Platform Solution for Concentrator 3.3.6 Platform Solution for Sub Station/Utility Servers 3.3.7 Network Topology and IP Addressing: WAN 3.3.8 Security on the Concentrator and Utility Servers 3.3.9 Summary on C-IoT Security 4 -- IOT REFERENCE DESIGN KIT 5 -- C-IOT CLOUD-BASED SERVICES AND END DEVICE DIVERSIITY 5.1 C-IoT Cloud Based Services 5.1.1 Introduction and Drivers to C-IoT Service Platform 5.1.2 Classes of C-IoT Cloud Computing 5.1.3 C-IoT Innovative and Collaborative Services 5.1.4 The Emerging Data Centre LAN 5.2 C-IoT User Device Diversity 5.2.1 Introduction 5.2.2 C-IoT Developers/Platform 5.2.3 Wearable Devices - Individual 5.2.4 Harvesting (Self-powered nodes) - Infrastructure Applications 5.2.5 Embedded Devices and Servers 5.2.6 Performing Sentiment Analysis Using Big Data 5.2.7 Far-Reaching Consequence 5.2.8 Collaboration 6 -- IMPACT OF C-IOT AND TIPS 6.1 Impact on Business Process Productivity and Smart of Digital Life 6.1.1 Individual 6.1.2 Industry 6.1.3 Infrastructure 6.2 Considerations of developing Differentiated C-IoT Solutions 6.2.1 Software Processes and Platform 6.2.3 Standardization 6.2.4 Advertising Ecosystem Value Exchange 6.2.5 Opportunity with Industry Supply Chain for Material Handling 6.3 Practical Tips in maintaining Digital Life Style 6.3.1 Mobile and Wearable Computing 6.3.2 Robotics and Automation 6.3.3 Sensors and C-IoT 6.3.4 BIG Data and Predictive Analysis 6.3.5 The Changing Workforce 6.3.6 Sustainability 7 -- CONCLUSION 7.1 Simple C-IoT Domains and Model 7.2 Disruptive Business Applications of C-IoT 7.3 A New LifeStyle 7.4 Development Platform 7.5 C-IoT emerging Standards, Consortiums and other Initiatives 7.5.1 C-IoT Emerging Standards 7.5.2 C-IoT Emerging Consortiums 7.5.3 Forums, Workshops, and other Initiatives 7.5.4 C-IoT and Radio Communications 7.5.5 C-IoT and Nanotechnology 7.5.6 C-IoT and Security 7.6 Final Note References About the Authors Index …”

Tabla de Contenidos: “…Critical Path -- Project Schedule -- Activity Slack -- Analyzing Cost-Time Trade‐Offs -- Cost to Crash -- Minimizing Costs -- Assessing and Analyzing Risks -- Risk‐Management Plans -- Managerial Practice 7.1 San Francisco-Oakland Bay Bridge -- Statistical Analysis -- Analyzing Probabilities -- Near‐Critical Paths -- Monitoring and Controlling Projects -- Monitoring Project Status -- Monitoring Project Resources -- Controlling Projects -- Learning Goals in Review -- MyLab Operations Management Resources -- Key Equations -- Key Terms -- Solved Problems -- Discussion Questions -- Problems -- Active Model Exercise -- Video Case Project Management at Choice Hotels International -- Case The Pert Mustang -- PART 2 Managing Customer Demand -- 8 FORECASTING -- Kimberly‐Clark -- Managing Demand -- Demand Patterns -- Demand Management Options -- Key Decisions on Making Forecasts -- Deciding What to Forecast -- Choosing the Type of Forecasting Technique -- Forecast Error -- Cumulative Sum of Forecast Errors -- Dispersion of Forecast Errors -- Mean Absolute Percent Error -- Computer Support -- Judgment Methods -- Causal Methods: Linear Regression -- Time‐Series Methods -- NaÏve Forecast -- Horizontal Patterns: Estimating the Average -- Trend Patterns: Using Regression -- Seasonal Patterns: Using Seasonal Factors -- Criteria for Selecting Time‐Series Methods -- Insights into Effective Demand Forecasting -- Big Data -- Managerial Practice 8.1 Big Data and Health Care Forecasting -- A Typical Forecasting Process -- Using Multiple Forecasting Methods -- Adding Collaboration to the Process -- Forecasting as a Nested Process -- Learning Goals in Review -- MyLab Operations Management Resources -- Key Equations -- Key Terms -- Solved Problems -- Discussion Questions -- Problems -- Video Case Forecasting and Supply Chain Management at Deckers Outdoor Corporation…”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Data Science and Python -- Chapter 1 Discovering the Match between Data Science and Python -- Understanding Python as a Language -- Viewing Python's various uses as a general-purpose language -- Interpreting Python -- Compiling Python -- Defining Data Science -- Considering the emergence of data science -- Outlining the core competencies of a data scientist -- Linking data science, big data, and AI -- Creating the Data Science Pipeline -- Understanding Python's Role in Data Science -- Considering the shifting profile of data scientists -- Working with a multipurpose, simple, and efficient language -- Learning to Use Python Fast -- Loading data -- Training a model -- Viewing a result -- Chapter 2 Introducing Python's Capabilities and Wonders -- Working with Python -- Contributing to data science -- Getting a taste of the language -- Understanding the need for indentation -- Working with Jupyter Notebook and Google Colab -- Performing Rapid Prototyping and Experimentation -- Considering Speed of Execution -- Visualizing Power -- Using the Python Ecosystem for Data Science -- Accessing scientific tools using SciPy -- Performing fundamental scientific computing using NumPy -- Performing data analysis using pandas -- Implementing machine learning using Scikit-learn -- Going for deep learning with Keras and TensorFlow -- Performing analysis efficiently using XGBoost -- Plotting the data using Matplotlib -- Creating graphs with NetworkX -- Chapter 3 Setting Up Python for Data Science -- Working with Anaconda -- Using Jupyter Notebook -- Accessing the Anaconda Prompt -- Installing Anaconda on Windows -- Installing Anaconda on Linux…”

Tabla de Contenidos: “…Cover; Title Page; Copyright Page; Contents; List of Figures and Tables; Preface; Who should read this book; Organization of the book; Guidelines for using this book; Conventions used in this book; Supplemental materials; Acknowledgments; Chapter one - Introduction; 1.1 - Big data; 1.2 - Web of data and the semantic web; 1.3 - RDF data management; 1.4 - Dimensions for comparing RDF stores; Chapter two - Database Management Systems; 2.1 - Technologies prevailing in the relational domain; 2.1.1 - Relational model; 2.1.2 - Indexes; 2.1.3 - Query processing; 2.1.4 - ACID transactions and OLTP…”