Databases / Data management Books

Book SynopsisUnderstand the different access control paradigms available in the Snowflake Data Cloud and learn how to implement access control in support of data privacy and compliance with regulations such as GDPR, APPI, CCPA, and SOX. The information in this book will help you and your organization adhere to privacy requirements that are important to consumers and becoming codified in the law. You will learn to protect your valuable data from those who should not see it while making it accessible to the analysts whom you trust to mine the data and create business value for your organization. Snowflake is increasingly the choice for companies looking to move to a data warehousing solution, and security is an increasing concern due to recent high-profile attacks. This book shows how to use Snowflake's wide range of features that support access control, making it easier to protect data access from the data origination point all the way to the presentation and visualization layer.Reading this book Table of Contents​Part I. Background1. What is Access Control?2. Data Types Requiring Access Control3. Data Privacy Laws and Regulatory Drivers4. Permission typesPart II. Creating Roles5. Functional Roles - What A Person Does6. Team Roles - Who A Person Is7. Assuming A Primary Role8. Secondary RolesPart III. Granting Permissions to Roles9. Role Inheritance10. Account and Database Level Privileges 11. Schema-Level Privileges12. Table and View Level Privileges13. Row-Level Permissioning and Fine-Grained Access Control14. Column-Level Permissioning and Data MaskingPart IV. Operationally Managing Access Control15. Secure Data Sharing16. Separating Production from Development17. Upstream & Downstream Services18. Managing Access Requests

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Book SynopsisGet introduced to Azure Arc-enabled Data Services and the powerful capabilities to deploy and manage local, on-premises, and hybrid cloud data resources using the same centralized management and tooling you get from the Azure cloud. This book shows how you can deploy and manage databases running on SQL Server and Postgres in your corporate data center or any cloud as if they were part of the Azure platform. This second edition has been updated to the latest codebase, allowing you to use this book as your handbook to get started with Azure Arc-enabled Data Services today. Learn how to benefit from Azure's centralized management, the automated rollout of patches and updates, managed backups, and more.This book is the perfect choice for anyone looking for a hybrid or multi-vendor cloud strategy for their data estate. The authors walk you through the possibilities and requirements to get Azure SQL Managed Instance and PostgresSQL Hyperscale deployed outside of Azure, so the services are accessible to companies that cannot move to the cloud or do not want to use the Microsoft cloud exclusively. The technology described in this book will benefit those required to keep sensitive services, such as medical databases, away from the public cloud equally as those who can't move to a public cloud for other reasons such as infrastructure constraints but still want to benefit from the Azure cloud and the centralized management and tooling that it supports. What You Will LearnUnderstand the fundamentals and architecture of Azure Arc-enabled data servicesBuild a multi-cloud strategy based on Azure Data ServicesDeploy Azure Arc-enabled data services on premises or in any cloudDeploy Azure Arc-enabled SQL Managed Instance on premises or in any cloudDeploy Azure Arc-enabled PostgreSQL Hyperscale on premises or in any cloudBackupand Restore your data that is managed by Azure Arc-enabled data servicesManage Azure-enabled data services running outside of AzureMonitor Azure-enabled data services through Grafana and KibanaMonitor Azure-enabled data services running outside of Azure through Azure MonitorWho This Book Is ForDatabase administrators and architects who want to manage on-premises or hybrid cloud data resources from the Microsoft Azure cloud. Especially for those wishing to take advantage of cloud technologies while keeping sensitive data on premises and under physical control.Table of Contents1. A Kubernetes Primer2. Azure Arc-Enabled Data Services3. Getting Ready for Deployment4. Installing Kubernetes5. Deploying a Data Controller in Indirect Mode 6. Deploying a Data Controller in Direct Mode 7. Deploying an Azure Arc-Enabled SQL Managed Instance8. Deploying Azure Arc-Enabled PostgreSQL Hyperscale 9. Monitoring and Management

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Book SynopsisThis book helps SAP architects and SAP Basis administrators deploy and operate SAP S/4HANA systems on the most common public cloud platforms. Market-leading cloud offerings are covered, including Amazon Web Services, Microsoft Azure, and Google Cloud. You will gain an end-to-end understanding of the initial implementation of SAP S/4HANA systems on those platforms. You will learn how to move away from the big monolithic SAP ERP systems and arrive at an environment with a central SAP S/4HANA system as the digital core surrounded by cloud-native services. The book begins by introducing the core concepts of Hyperscaler cloud platforms that are relevant to SAP. You will learn about the architecture of SAP S/4HANA systems on public cloud platforms, with specific content provided for each of the major platforms. The book simplifies the deployment of SAP S/4HANA systems in public clouds by providing step-by-step instructions and helping you deal with thecomplexity of such a deployment. ConteTable of Contents1. Introduction to Public Cloud and Hyperscalers2. SAP S/4HANA systems on Public Cloud3. SAP S/4HANA Deployment and Migration4. SAP S/4HANA on AWS Elastic Compute Cloud – Concepts and Architecture5. SAP S/4HANA on AWS Elastic Compute Cloud – Deployment 6. SAP S/4HANA on Microsoft Azure – Concepts and Architecture7. SAP S/4HANA on Microsoft Azure – Deployment 8. SAP S/4HANA on Google Cloud – Concepts and Architecture9. SAP S/4HANA on Google Cloud – Deployment and Setup10. Summary and Outlook

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Book SynopsisThis book provides all you need to find data from external sources and load and transform that data into Power BI where you can mine it for business insights and a competitive edge. This ranges from connecting to corporate databases such as Azure SQL and SQL Server to file-based data sources, and cloud- and web-based data sources. The book also explains the use of Direct Query and Live Connect to establish instant connections to databases and data warehouses and avoid loading data.The book provides detailed guidance on techniques for transforming inbound data into normalized data sets that are easy to query and analyze. This covers data cleansing, data modification, and standardization as well as merging source data into robust data structures that can feed into your data model. You will learn how to pivot and transpose data and extrapolate missing values as well as harness external programs such as R and Python into a Power Query data flow. You also will see how to handle errors in soTable of Contents1. Discovering and Loading Data with Power BI Desktop2. Discovering and Loading File-Based Data with Power BI Desktop3. Loading Data From Databases and Data Warehouses4. DirectQuery and Live Connect5. Loading Data from the Web and Cloud6. Loading Data from Other Data Sources7. Power Query8. Structuring Data9. Shaping Data10. Data Cleansing11. Data Transformation12. Complex Data Structures13. Organizing, Managing, and Parameterizing Queries14. The M LanguageAppendix A: Sample Data

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Book SynopsisTake a deep dive into the Azure SQL Database Hyperscale Service Tier and discover a new form of cloud architecture from Microsoft that supports massive databases. The new horizontally scalable architecture, formerly code-named Socrates, allows you to decouple compute nodes from storage layers. This radically different approach dramatically increases the scalability of the service. This book shows you how to leverage Hyperscale to provide next-level scalability, high throughput, and fast performance from large databases in your environment. The book begins by showing how Hyperscale helps you eliminate many of the problems of traditional high-availability and disaster recovery architecture. You''ll learn how Hyperscale overcomes storage capacity limitations and issues with scale-up times and costs. With Hyperscale, your costs do not increase linearly with database size and you can manage more data than ever at a lower cost. The book teaches you how tTable of ContentsIntroductionPart I. Architecture.1. The Journey to Hyperscale Architecture in Azure SQL2. Azure SQL Hyperscale Architecture: Concepts and FoundationsPart II. Planning and Deployment3. Planning an Azure SQL DB Hyperscale Environment 4. Deploying a Highly Available Hyperscale Database into a Virtual Network 5. Administering a Hyperscale Database in a Virtual Network in the Azure Portal6. Configuring Transparent Data Encryption to Bring Your Own Key7. Enabling Geo-replication for Disaster Recovery8. Configuring Security Features and Enabling Diagnostic and Audit Logs9. Deploying Azure SQL DB Hyperscale using PowerShell10. Deploying Azure SQL DB Hyperscale using Bash and Azure CLI11. Deploying Azure SQL DB Hyperscale using Azure Bicep12. Testing Hyperscale Database Performance Against Other Azure SQL Deployment OptionsPart III. Operation and Management13. Monitoring and Scaling 14. Backup, Restore and Disaster Recovery15. Security and Updating16. Managing CostsPart IV. Migration17. Determining whether Hyperscale is Appropriate 18. Migrating to Hyperscale19. Reverse Migrating Away from HyperscaleConclusion

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Book SynopsisUnderstand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance-all designed to deliver data as a product within hybrid cloud landscapes.This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses artificial intelligence (AI) and machine learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience.By the end of this book, you will understand the data fabric concept and architecture as it relates to themes such as automated unified dTable of ContentsPart I – Data Fabric FoundationChapter 1: Evolution of Data ArchitectureChapter 2: Terminology – Data Fabric and Data MeshChapter 3: Data Fabric and Data Mesh Use Case ScenariosChapter 4: Data Fabric and Data Mesh Business BenefitsPart II – Key Data Fabric Capabilities and ConceptsChapter 5: Key Data Fabric and Data Mesh CapabilitiesChapter 6: Relevant AI and ML ConceptsChapter 7: AI/ML for a Data Fabric and Data MeshChapter 8: AI for Entity ResolutionChapter 9: Data Fabric and Data Mesh for the AI LifecyclePart III – Deploying Data Fabric Solutions in ContextChapter 10: Data Fabric Architecture PatternsChapter 11: Role of Data Fabric within an Enterprise Architecture\Chapter 12: Data Fabric and Data Mesh in Hybrid Cloud LandscapeChapter 13: Intelligent Cataloging and Metadata ManagementChapter 14: Automated Data Fabric and Data Mesh AspectsChapter 15: Data Governance in the Context of Data Fabric and Data MeshPart IV – Current Offerings and Future AspectsChapter 16: Sample Vendor OfferingsChapter 17: Data Fabric and Data Mesh Research AreasChapter 18: In Summary and OnwardsAbbreviations.

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Book Synopsis1. An Introduction to Data Analysis .- 2. Introduction to the Python's World.- 3. The NumPy Library .- 4. The pandas Library-- An Introduction.- 5. pandas: Reading and Writing Data .- 6. pandas in Depth: Data Manipulation .- 7. Data Visualization with matplotlib .- 8. Machine Learning with scikit-learn.- 9. Deep Learning with TensorFlow.- 10. An Example - Meteorological Data.- 11. Embedding the JavaScript D3 Library in IPython Notebook.- 12. Recognizing Handwritten Digits.- 13. Textual data Analysis with NLTK.- 14. Image Analysis and Computer Vision with OpenCV.- Appendix A.- Appendix B.Table of ContentsPython Data Analytics1. An Introduction to Data Analysis 2. Introduction to the Python's World3. The NumPy Library 4. The pandas Library-- An Introduction5. pandas: Reading and Writing Data 6. pandas in Depth: Data Manipulation 7. Data Visualization with matplotlib 8. Machine Learning with scikit-learn9. Deep Learning with TensorFlow10. An Example - Meteorological Data11. Embedding the JavaScript D3 Library in IPython Notebook12. Recognizing Handwritten Digits13. Textual data Analysis with NLTK 14. Image Analysis and Computer Vision with OpenCV Appendix A Appendix B

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Book SynopsisThis book provides detailed guidance around architecting and deploying Power BI reporting solutions, including help and best practices for sharing and security. You''ll find chapters on dataflows, shared datasets, composite model and DirectQuery connections to Power BI datasets, deployment pipelines, XMLA endpoints, and many other important features related to the overall Power BI architecture that are new since the first edition. You will gain an understanding of what functionality each of the Power BI components provide (such as Dataflow, Shared Dataset, Datamart, thin reports, and paginated reports), so that you can make an informed decision about what components to use in your solution. You will get to know the pros and cons of each component, and how they all work together within the larger Power BI architecture.Commonly encountered problems you will learn to handle include content unexpectedly changing while users are in the process of creating reports and bTable of ContentsIntroductionPart I. Getting Started1. Power BI Ecosystem and Components2. Tools and PreparationPart II. Development3. Import Data or Schedule Refresh4. DirectQuery 5. Live Connection6. Composite Mode7. Choosing the Right Connection Type8. Dataflows9. Shared Datasets10. Multi-Developer Architecture11. Hybrid Architecture using other Microsoft Services12. DirectQuery to Power BI Dataset13. Dataflow Development Architecture14. Analyze in Excel15. Development Tools16. Power BI Helper for Developers17. Dataset Types18. Realtime Power BI Solution19. Paginated Reports20. Power BI Templates21. Power BI Desktop Development Templates22. Incremental Refresh23. Big Data Considerations, Hybrid Tables, and PerformancePart III. Deployment24. Power BI Service Content25. Power BI Report Server26. Gateway27. Power BI Licensing Guide28. Power BI Premium29. Premium Per User30. Premium Settings and Configuration31. Tenant Settings32. Administrator Reports and Metrics33. Workspace Structure and Architecture34. Workspace Rules35. Deployment Pipeines36. REST API for Deployment and Architecture37. Power BI Helper for Deployment and Administration38. XMLA EndpointPart IV. Sharing and Security39. Governance40. Dashboard and Report Sharing41. Workspaces as Collaborative Environments42. Power BI Apps43. Embed Code and Publish to Web44. Embed in SharePoint Online45. Microsoft Teams Integration46. Power BI Embedded47. SharePoint Online Integration48. Microsoft Office49. Comparing Power BI Sharing Methods50. Usage Metrics Reports51. Usage Metrics using REST API52. Usage Metrics using Power BI Helper53. Row Level Security54. Dynamic Row Level Security55. Object-Level Security

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Book SynopsisThis book will help prepare you for the Microsoft DP-420 exam. Whether you are new to Azure Cosmos DB or have experience working with the platform, Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB is organized to address the specific skills measured in the DP-420 exam. The topics covered include NoSQL models, code, and real-world scenarios aimed at helping you to understand and solve the case studies included in the exam.Beyond the exam, this book will assist you in your journey to adopt Microsoft Azure Cosmos DB for your own projects. You'll learn what makes Azure Cosmos DB such a robust NoSQL service, as well as how NoSQL approaches help enable modern applications. You'll also get practical guidance for your own implementations. The topics covered in this book are essential to knowing how to leverage the Cosmos DB service and provide best practices that will guide you to success both on the exam and in your career. What You Will LearnUnderstand aTable of Contents1. Scheduling and Taking the DP-420 Exam2. Design and Implement a Non-Relational Data Model3. Design a Data Partitioning Strategy4. Plan and implement Sizing and Scaling5. Implement Client Connectivity Options 6. Implement Data Access with Cosmos DB SQL 7. Implement Data Access with SQL API SDKs8. Implement Server-Side Programming9. Design and Implement a Replication Strategy 10. Design and Implement Multi-Region Write11. Enable Analytical Workloads 12. Implement Solutions Across Services13. Optimize Query Performance 14. Design and Implement Change Feeds 15. Define and Implement an Indexing Strategy 16. Monitor and Troubleshoot17. Implement Backup and Restore 18. Implement Security19. Implement Data Movement 20. Implement a DevOps Process

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Book SynopsisFrom understanding various technologies as an enabler to marketing efforts and its impact on decision making and mapping of various facets of customer experience, this book is recommended for marketers and learners to understand the advantages of using technology.Table of Contents1. From Data to Action: Leveraging AI in marketing1.1 AI & Marketing: Core Elements 1.2 Unleashing AI driven competitive advantage through IoT and Big Data Analytics1.3 Challenges of using AI technologies in the area of Marketing1.4 Core benefits of AI Marketing1.5 AI and future of Marketing 2. Informed Data driven decision making 2.1 Using Big data analytics for market intelligence2.2 Application of Big data analytics to marketing mix elements2.3 AI led Cognitive Data Quality Management2.4 AI-enabled marketing decisions 3. AI Marketing & Predicting Consumer Choices3.1 The value of social media for Improving Customer Engagement3.2 Optimizing marketing value, retention, customer satisfaction and loyalty3.3 Strategic applications of AI in different stages of customer journey3.4 AI in segmentation, targeting and positioning3.5 Internet trends and customer sentiment analysis 4. Unlocking Data in understanding Customers4.1 Customer Analytics4.1.1 Descriptive Customer Analytics4.1.2 Predictive Customer Analytics4.1.3 Prescriptive Customer Analytics4.2 Marketing Analytics: AI for Data Driven Marketing4.3 Customer Data Visualization & Information Management4.4 Mapping Customer Journey through big data analytics 5. Improving Experiences and Customer Satisfaction with AI5.1 AI and Product Life Cycle Management (PLM)5.2 Opportunities and Challenges of applying AI for PLM5.3 AI and granular personalization5.4 Use of AI to provide each segment of a target with tailored content 6. Value Creation & Value Capture with Artificial Intelligence6.1 Role of AI in optimizing Pricing6.2 Optimizing marketing value, retention and loyalty6.3 XR on value co-creation and customer engagement6.4 Creating value with data analytics6.5 Customer Value Modelling6.6 Marketing intelligence for optimal marketing return6.7 Creating value with data analytics 7. Reliable & Profitable AI driven Distribution7.1 Using AI for Distribution Process Management7.2 Smart Distribution7.3 Prediction of consumer behavior and improving lead generation7.4 Optimizing sales territory design with AI7.5 AI based delivery system7.6 AI integrated Logistics, inventory management, warehousing and transportation 8. Artificial Intelligence driven Promotions and Social Networking8.1 Network Modelling, Visualization and Analyzing Tools8.2 Role of Centrality in Social Networks: Influencer Marketing8.3 Sentiment Analysis and Public Opinion Mining8.4 Review Mining and Rating8.5 Big Data & scalability in Social Networks8.6 AI powered Chatbots and conversational experiences8.7 Propensity modelling for advertisement targeting and lead scoring8.8 Advertising Optimization & Viral Effects8.9 Fake News, Misinformation & Rumor Detection 9. Optimizing the future of Digital Marketing with A.I.9.1 Enhancing Interactive User Experience with AI9.2 Content Creation & Curation with AI9.3 Aligning marketing metrics with business goals9.4 Web analytics for digital marketing 10. Ethics of Artificial Intelligence for Marketing10.1 Dark side of AI in Marketing10.1.1 Consumers’ data protection rights10.1.2 Concerns about AI-enabled marketing decisions 10.1.3 Legal Concerns and Compliance issues10.2 Piracy, Security and Consumerism10.3 Ethical, Moral & Societal Challenges of AI 11. Case Studies on applications of AI11.1 AI driven cyber security and privacy11.2 Applications of AI in health care11.3 Applications of AI in tourism11.4 Applications of AI in manufacturing11.5 Applications of AI in finance

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Book SynopsisThis practical book not only shows Hadoop administrators and security architects how to protect Hadoop data from unauthorized access, it also shows how to limit the ability of an attacker to corrupt or modify data in the event of a security breach.

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Book SynopsisThis hands-on guide shows developers and systems administrators familiar with Hadoop how to install, use, and manage cloud-born clusters efficiently. You'll learn how to architect clusters that work with cloud-provider featuresnot just to avoid pitfalls, but also to take full advantage of these services.

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Book SynopsisThis guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable

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Book SynopsisAuthor Lorna Brown provides more than 100 practical recipes to enhance the way you build Tableau dashboards--and helps you understand your data through the power of Tableau Desktop 2020's interactive data visualizations.

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Book SynopsisThe datafication of our world offers huge challenges and opportunities for social science. The ‘data-drivenness’ of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the ‘quantitative’ dimensions of big data with the ‘qualitative’ sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry. In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways. Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.Trade Review�In this elegant book, Lindgren moves beyond the frequent schizophrenia of methods debates to ask: what happens when traditional social theory and data analytics are combined smartly? The result is illuminating and useful. Highly recommended!� Nick Couldry, London School of Economics and Political Science �This is a very interesting book with an original approach which will be useful to scholars and students.� Lina Dencik, Cardiff University �In this provocative text, Lindgren leads us on an innovative path that should both challenge and inspire researchers across the quant-qual divide. A new social science methods classic for the digital media era!� Sarah T. Roberts, UCLATable of ContentsIntroduction: Data Theory1 Beyond Method2 Decoding Social Forms3 Unintended Consequences4 Actor-Networks5 Collective Presentations6 Symbolic Power7 Theoretical I/O Conclusion: Theory/DataReferencesIndex

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Book SynopsisThe datafication of our world offers huge challenges and opportunities for social science. The ‘data-drivenness’ of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the ‘quantitative’ dimensions of big data with the ‘qualitative’ sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry. In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways. Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.Trade Review�In this elegant book, Lindgren moves beyond the frequent schizophrenia of methods debates to ask: what happens when traditional social theory and data analytics are combined smartly? The result is illuminating and useful. Highly recommended!� Nick Couldry, London School of Economics and Political Science �This is a very interesting book with an original approach which will be useful to scholars and students.� Lina Dencik, Cardiff University �In this provocative text, Lindgren leads us on an innovative path that should both challenge and inspire researchers across the quant-qual divide. A new social science methods classic for the digital media era!� Sarah T. Roberts, UCLATable of ContentsIntroduction: Data Theory 1 Beyond Method 2 Decoding Social Forms 3 Unintended Consequences 4 Actor-Networks 5 Collective Presentations 6 Symbolic Power 7 Theoretical I/O Conclusion: Theory/Data References Index

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Book SynopsisThis book gives a full treatment of databases, managing the total syllabuses for both a starting course and a propelled course on databases. It offers an adjusted perspective of ideas, dialects/languages and models, with solid reference to current innovation what's more, to business database management systems (DBMSs).It is intended to clarify the standards of information administration and for instruct how to ace two fundamental abilities: how to inquiry a database (and compose programming that includes database get to) and how to outline its blueprint structure.

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Book SynopsisThis book provides an in-depth study of the advanced concepts and technologies in database systems. It covers topics such as distributed databases, object-oriented databases, data mining, and big data analytics. The book is written for students and professionals in the field of computer science who want to enhance their knowledge and skills in advanced database technologies. This book will provide you with a solid understanding of the latest developments in database systems and their applications.Table of Contents Chapter 1 Introduction to Database Systems Chapter 2 Types of Databases Chapter 3 Database Modeling Chapter 4 Big Data Analytics Chapter 5 Stream Processing Systems Chapter 6 Cloud-Based Database Systems Chapter 7 Main Memory Database System Chapter 8 Advanced Database Security

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Book SynopsisThis book will be a "must" for people who want good knowledge of big data concepts and their applications in the real world, particularly in the field of insurance. It will be useful to people working in finance and to masters students using big data tools. The authors present the bases of big data: data analysis methods, learning processes, application to insurance and position within the insurance market. Individual chapters a will be written by well-known authors in this field.Table of ContentsForeword xiJean-Charles POMEROL Introduction xiiiMarine CORLOSQUET-HABART and Jacques JANSSEN Chapter 1. Introduction to Big Data and Its Applications in Insurance 1Romain BILLOT, Cécile BOTHOREL and Philippe LENCA 1.1. The explosion of data: a typical day in the 2010s 1 1.2. How is big data defined? 4 1.3. Characterizing big data with the five Vs 5 1.3.1. Variety 6 1.3.2. Volume 7 1.3.3. Velocity 9 1.3.4. Towards the five Vs: veracity and value 9 1.3.5. Other possible Vs 11 1.4. Architecture 11 1.4.1. An increasingly complex technical ecosystem 12 1.4.2. Migration towards a data-oriented strategy 17 1.4.3. Is migration towards a big data architecture necessary? 18 1.5. Challenges and opportunities for the world of insurance 20 1.6. Conclusion 22 1.7. Bibliography 23 Chapter 2. From Conventional Data Analysis Methods to Big Data Analytics 27Gilbert SAPORTA 2.1. From data analysis to data mining: exploring and predicting 27 2.2. Obsolete approaches 28 2.3. Understanding or predicting? 30 2.4. Validation of predictive models 30 2.4.1. Elements of learning theory 31 2.4.2. Cross-validation 34 2.5. Combination of models 34 2.6. The high dimension case 36 2.6.1. Regularized regressions 36 2.6.2. Sparse methods 38 2.7. The end of science? 39 2.8. Bibliography 40 Chapter 3. Statistical Learning Methods 43Franck VERMET 3.1. Introduction 43 3.1.1. Supervised learning 44 3.1.2. Unsupervised learning 46 3.2. Decision trees 46 3.3. Neural networks 49 3.3.1. From real to formal neuron 50 3.3.2. Simple Perceptron as linear separator 52 3.3.3. Multilayer Perceptron as a function approximation tool 54 3.3.4. The gradient backpropagation algorithm 56 3.4. Support vector machines (SVM) 62 3.4.1. Linear separator 62 3.4.2. Nonlinear separator 66 3.5. Model aggregation methods 66 3.5.1. Bagging 67 3.5.2. Random forests 69 3.5.3. Boosting 70 3.5.4. Stacking 74 3.6. Kohonen unsupervised classification algorithm 74 3.6.1. Notations and definition of the model 76 3.6.2. Kohonen algorithm 77 3.6.3. Applications 79 3.7. Bibliography 79 Chapter 4. Current Vision and Market Prospective 83Florence PICARD 4.1. The insurance market: structured, regulated and long-term perspective 83 4.1.1. A highly regulated and controlled profession 84 4.1.2. A wide range of long-term activities 85 4.1.3. A market related to economic activity 87 4.1.4. Products that are contracts: a business based on the law 87 4.1.5. An economic model based on data and actuarial expertise 88 4.2. Big data context: new uses, new behaviors and new economic models 89 4.2.1. Impact of big data on insurance companies 90 4.2.2. Big data and digital: a profound societal change 91 4.2.3. Client confidence in algorithms and technology 93 4.2.4. Some sort of negligence as regards the possible consequences of digital traces 94 4.2.5. New economic models 95 4.3. Opportunities: new methods, new offers, new insurable risks, new management tools 95 4.3.1. New data processing methods 96 4.3.2. Personalized marketing and refined prices 98 4.3.3. New offers based on new criteria 100 4.3.4. New risks to be insured 101 4.3.5. New methods to better serve and manage clients 102 4.4. Risks weakening of the business: competition from new actors, “uberization”, contraction of market volume 103 4.4.1. The risk of demutualization 103 4.4.2. The risk of “uberization” 104 4.4.3. The risk of an omniscient “Google” in the dominant position due to data 105 4.4.4. The risk of competition with new companies created for a digital world 105 4.4.5. The risk of reduction in the scope of property insurance 106 4.4.6. The risk of non-access to data or prohibition of use 107 4.4.7. The risk of cyber attacks and the risk of non-compliance 108 4.4.8. Risks of internal rigidities and training efforts to implement 109 4.5. Ethical and trust issues 109 4.5.1. Ethical charter and labeling: proof of loyalty 110 4.5.2. Price, ethics and trust 112 4.6. Mobilization of insurers in view of big data 113 4.6.1. A first-phase “new converts” 113 4.6.2. A phase of appropriation and experimentation in different fields 115 4.6.3. Changes in organization and management and major training efforts to be carried out 118 4.6.4. A new form of insurance: “connected” insurance 118 4.6.5. Insurtech and collaborative economy press for innovation 121 4.7. Strategy avenues for the future 122 4.7.1. Paradoxes and anticipation difficulties 122 4.7.2. Several possible choices 123 4.7.3. Unavoidable developments 127 4.8. Bibliography 128 Chapter 5. Using Big Data in Insurance 131Emmanuel BERTHELÉ 5.1. Insurance, an industry particularly suited to the development of big data 131 5.1.1. An industry that has developed through the use of data 131 5.1.2. Link between data and insurable assets 136 5.1.3. Multiplication of data sources of potential interest 138 5.2. Examples of application in different insurance activities 141 5.2.1. Use for pricing purposes and product offer orientation 142 5.2.2. Automobile insurance and telematics 143 5.2.3. Index-based insurance of weather-sensitive events 145 5.2.4. Orientation of savings in life insurance in a context of low interest rates 146 5.2.5. Fight against fraud 148 5.2.6. Asset management 150 5.2.7. Reinsurance 150 5.3. New professions and evolution of induced organizations for insurance companies 151 5.3.1. New professions related to data management, processing and valuation 151 5.3.2. Development of partnerships between insurers and third-party companies 153 5.4. Development constraints 153 5.4.1. Constraints specific to the insurance industry 153 5.4.2. Constraints non-specific to the insurance industry 155 5.4.3. Constraints, according to the purposes, with regard to the types of algorithms used 158 5.4.4. Scarcity of profiles and main differences with actuaries 159 5.5. Bibliography 161 List of Authors 163 Index 165

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Book SynopsisThe topic of NoSQL databases has recently emerged, to face the Big Data challenge, namely the ever increasing volume of data to be handled. It is now recognized that relational databases are not appropriate in this context, implying that new database models and techniques are needed. This book presents recent research works, covering the following basic aspects: semantic data management, graph databases, and big data management in cloud environments. The chapters in this book report on research about the evolution of basic concepts such as data models, query languages, and new challenges regarding implementation issues.Table of ContentsForeword xiAnne LAURENT and Dominique LAURENT Preface xiiiOlivier PIVERT Chapter 1. NoSQL Languages and Systems 1Kim NGUYỄN 1.1. Introduction 1 1.1.1. The rise of NoSQL systems and languages 1 1.1.2. Overview of NoSQL concepts 4 1.1.3. Current trends of French research in NoSQL languages 6 1.2. Join implementations on top of MapReduce 7 1.3. Models for NoSQL languages and systems 12 1.4. New challenges for database research 16 1.5. Bibliography 18 Chapter 2. Distributed SPARQL Query Processing: A Case Study with Apache Spark 21Bernd AMANN, Olivier CURÉ and Hubert NAACKE 2.1. Introduction 21 2.2. RDF and SPARQL 22 2.2.1. RDF framework and data model 22 2.2.2. SPARQL query language 25 2.3. SPARQL query processing 29 2.3.1. SPARQL with and without RDF/S entailment 29 2.3.2. Query optimization 30 2.3.3. Triple store systems 33 2.4. SPARQL and MapReduce 34 2.4.1. MapReduce-based SPARQL processing 35 2.4.2. Related work 39 2.5. SPARQL on Apache Spark 41 2.5.1. Apache Spark 41 2.5.2. SPARQL on Spark 42 2.5.3. Experimental evaluation 48 2.6. Bibliography 53 Chapter 3. Doing Web Data: from Dataset Recommendation to Data Linking 57Manel ACHICHI, Mohamed BEN ELLEFI, Zohra BELLAHSENE and Konstantin TODOROV 3.1. Introduction 57 3.1.1. The Semantic Web vision 57 3.1.2. Linked data life cycles 58 3.1.3. Chapter overview 61 3.2. Datasets recommendation for data linking 62 3.2.1. Process definition 63 3.2.2. Dataset recommendation for data linking based on a Semantic Web index 64 3.2.3. Dataset recommendation for data linking based on social networks 64 3.2.4. Dataset recommendation for data linking based on domain-specific keywords 65 3.2.5. Dataset recommendation for data linking based on topic modeling 65 3.2.6. Dataset recommendation for data linking based on topic profiles 66 3.2.7. Dataset recommendation for data linking based on intensional profiling 67 3.2.8. Discussion on dataset recommendation approaches 68 3.3. Challenges of linking data 69 3.3.1. Value dimension 70 3.3.2. Ontological dimension 74 3.3.3. Logical dimension 77 3.4. Techniques applied to the data linking process 78 3.4.1. Data linking techniques 79 3.4.2. Discussion 83 3.5. Conclusion 86 3.6. Bibliography 87 Chapter 4. Big Data Integration in Cloud Environments: Requirements, Solutions and Challenges 93Rami SELLAMI and Bruno DEFUDE 4.1. Introduction 93 4.2. Big Data integration requirements in Cloud environments 96 4.3. Automatic data store selection and discovery 99 4.3.1. Introduction 99 4.3.2. Model-based approaches 99 4.3.3. Matching-oriented approaches 100 4.3.4. Comparison 102 4.4. Unique access for all data stores 103 4.4.1. Introduction 103 4.4.2. ODBAPI: A unified REST API for relational and NoSQL data stores 104 4.4.3. Other works 105 4.4.4. Comparison 107 4.5. Unified data model and query languages 108 4.5.1. Introduction 108 4.5.2. Data models of classical data integration approaches 109 4.5.3. A global schema to unify the view over relational and NoSQL data stores 110 4.5.4. Other works 113 4.5.5. Comparison 117 4.6. Query processing and optimization 118 4.6.1. Introduction 118 4.6.2. Federated query language approaches 118 4.6.3. Integrated query language approaches 121 4.6.4. Comparison 124 4.7. Summary and open issues 125 4.7.1. Summary 125 4.7.2. Open issues 127 4.8. Conclusion 129 4.9. Bibliography 129 Chapter 5. Querying RDF Data: A Multigraph-based Approach 135Vijay INGALALLI, Dino IENCO and Pascal PONCELET 5.1. Introduction 135 5.2. Related work 137 5.3. Background and preliminaries 137 5.3.1. RDF data 138 5.3.2. SPARQL query 140 5.3.3. SPARQL querying by adopting multigraph homomorphism 142 5.4. AMBER: A SPARQL querying engine 143 5.5. Index construction 144 5.5.1. Attribute index 144 5.5.2. Vertex signature index 145 5.5.3. Vertex neighborhood index 148 5.6. Query matching procedure 149 5.6.1. Vertex-level processing 151 5.6.2. Processing satellite vertices 152 5.6.3. Arbitrary query processing 154 5.7. Experimental analysis 159 5.7.1. Experimental setup 159 5.7.2. Workload generation 160 5.7.3. Comparison with RDF engines 161 5.8. Conclusion 164 5.9. Acknowledgment 164 5.10. Bibliography 164 Chapter 6. Fuzzy Preference Queries to NoSQL Graph Databases 167Arnaud CASTELLTORT, Anne LAURENT, Olivier PIVERT, Olfa SLAMA and Virginie THION 6.1. Introduction 167 6.2. Preliminary statements 168 6.2.1. Graph databases 168 6.2.2. Fuzzy set theory 174 6.3. Fuzzy preference queries over graph databases 176 6.3.1. Fuzzy preference queries over crisp graph databases 176 6.3.2. Fuzzy preference queries over fuzzy graph databases 182 6.4. Implementation challenges 193 6.4.1. Modeling fuzzy databases 193 6.4.2. Evaluation of queries with fuzzy preferences 193 6.4.3. Scalability 195 6.5. Related work 197 6.6. Conclusion and perspectives 198 6.7. Acknowledgment 199 6.8. Bibliography 199 Chapter 7. Relevant Filtering in a Distributed Content-based Publish/Subscribe System 203Cédric DU MOUZA and Nicolas TRAVERS 7.1. Introduction 203 7.2. Related work: novelty and diversity filtering 205 7.3. A Publish/Subscribe data model 206 7.3.1. Data model 206 7.3.2. Weighting terms in textual data flows 207 7.4. Publish/Subscribe relevance 208 7.4.1. Items and histories 208 7.4.2. Novelty 209 7.4.3. Diversity 209 7.4.4. An overview of the filtering process 210 7.4.5. Choices of relevance 210 7.5. Real-time integration of novelty and diversity 212 7.5.1. Centralized implementation 212 7.5.2. Distributed filtering 216 7.6. TDV updates 221 7.6.1. TDV computation techniques 221 7.6.2. Incremental approach 223 7.6.3. TDV in a distributed environment 225 7.7. Experiments 228 7.7.1. Implementation and description of datasets 229 7.7.2. TDV updates 229 7.7.3. Filtering rate 230 7.7.4. Performance evaluation in the centralized environment 234 7.7.5. Performance evaluation in a distributed environment 238 7.7.6. Quality of filtering 240 7.8. Conclusion 241 7.9. Bibliography 242 List of Authors 245 Index 247

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Book SynopsisBig Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.Table of ContentsAcknowledgments xi Introduction xiii Chapter 1 Optimization and Big Data 1 1.1 Context of Big Data 1 1.1.1 Examples of situations 2 1.1.2 Definitions 3 1.1.3 Big Data challenges 5 1.1.4 Metaheuristics and Big Data 8 1.2 Knowledge discovery in Big Data 10 1.2.1 Data mining versus knowledge discovery 10 1.2.2 Main data mining tasks 12 1.2.3 Data mining tasks as optimization problems 16 1.3 Performance analysis of data mining algorithms 17 1.3.1 Context 17 1.3.2 Evaluation among one or several dataset(s) 18 1.3.3 Repositories and datasets 20 1.4 Conclusion 21 Chapter 2 Metaheuristics – A Short Introduction 23 2.1 Introduction 24 2.1.1 Combinatorial optimization problems 24 2.1.2 Solving a combinatorial optimization problem 25 2.1.3 Main types of optimization methods 25 2.2 Common concepts of metaheuristics 26 2.2.1 Representation/encoding 27 2.2.2 Constraint satisfaction 28 2.2.3 Optimization criterion/objective function 28 2.2.4 Performance analysis 29 2.3 Single solution-based/local search methods 31 2.3.1 Neighborhood of a solution 31 2.3.2 Hill climbing algorithm 33 2.3.3 Tabu Search 34 2.3.4 Simulated annealing and threshold acceptance approach 35 2.3.5 Combining local search approaches 36 2.4 Population-based metaheuristics 38 2.4.1 Evolutionary computation 38 2.4.2 Swarm intelligence 41 2.5 Multi-objective metaheuristics 43 2.5.1 Basic notions in multi-objective optimization 44 2.5.2 Multi-objective optimization using metaheuristics 47 2.5.3 Performance assessment in multi-objective optimization 51 2.6 Conclusion 52 Chapter 3 Metaheuristics and Parallel Optimization 53 3.1 Parallelism 53 3.1.1 Bit-level 53 3.1.2 Instruction-level parallelism 54 3.1.3 Task and data parallelism 54 3.2 Parallel metaheuristics 55 3.2.1 General concepts 55 3.2.2 Parallel single solution-based metaheuristics 55 3.2.3 Parallel population-based metaheuristics 57 3.3 Infrastructure and technologies for parallel metaheuristics 57 3.3.1 Distributed model 57 3.3.2 Hardware model 58 3.4 Quality measures 60 3.4.1 Speedup 60 3.4.2 Efficiency 61 3.4.3 Serial fraction 61 3.5 Conclusion 61 Chapter 4 Metaheuristics and Clustering 63 4.1 Task description 63 4.1.1 Partitioning methods 65 4.1.2 Hierarchical methods 66 4.1.3 Grid-based methods 67 4.1.4 Density-based methods 67 4.2 Big Data and clustering 68 4.3 Optimization model 68 4.3.1 A combinatorial problem 69 4.3.2 Quality measures 69 4.3.3 Representation 76 4.4 Overview of methods 81 4.5 Validation 82 4.5.1 Internal validation 84 4.5.2 External validation 84 4.6 Conclusion 86 Chapter 5 Metaheuristics and Association Rules 87 5.1 Task description and classical approaches 88 5.1.1 Initial problem 88 5.1.2 A priori algorithm 89 5.2 Optimization model 90 5.2.1 A combinatorial problem 90 5.2.2 Quality measures 90 5.2.3 A mono- or a multi-objective problem? 91 5.3 Overview of metaheuristics for the association rules mining problem 93 5.3.1 Generalities 93 5.3.2 Metaheuristics for categorical association rules 94 5.3.3 Evolutionary algorithms for quantitative association rules 99 5.3.4 Metaheuristics for fuzzy association rules 102 5.4 General table 105 5.5 Conclusion 107 Chapter 6 Metaheuristics and (Supervised) Classification 109 6.1 Task description and standard approaches 110 6.1.1 Problem description 110 6.1.2 K-nearest neighbor 110 6.1.3 Decision trees 111 6.1.4 Naive Bayes 112 6.1.5 Artificial neural networks 113 6.1.6 Support vector machines 114 6.2 Optimization model 114 6.2.1 A combinatorial problem 114 6.2.2 Quality measures 114 6.2.3 Methodology of performance evaluation in supervised classification 117 6.3 Metaheuristics to build standard classifiers 118 6.3.1 Optimization of K-NN 118 6.3.2 Decision tree 119 6.3.3 Optimization of ANN 122 6.3.4 Optimization of SVM 124 6.4 Metaheuristics for classification rules 126 6.4.1 Modeling 126 6.4.2 Objective function(s) 127 6.4.3 Operators 129 6.4.4 Algorithms 130 6.5 Conclusion 132 Chapter 7 On the Use of Metaheuristics for Feature Selection in Classification 135 7.1 Task description 136 7.1.1 Filter models 136 7.1.2 Wrapper models 137 7.1.3 Embedded models 137 7.2 Optimization model 138 7.2.1 A combinatorial optimization problem 138 7.2.2 Representation 139 7.2.3 Operators 140 7.2.4 Quality measures 140 7.2.5 Validation 143 7.3 Overview of methods 143 7.4 Conclusion 144 Chapter 8 Frameworks 147 8.1 Frameworks for designing metaheuristics 147 8.1.1 Easylocal++ 148 8.1.2 HeuristicLab 148 8.1.3 jMetal 149 8.1.4 Mallba 149 8.1.5 ParadisEO 150 8.1.6 ECJ 150 8.1.7 OpenBeagle 151 8.1.8 JCLEC 151 8.2 Framework for data mining 151 8.2.1 Orange 152 8.2.2 R and Rattle GUI 153 8.3 Framework for data mining with metaheuristics 153 8.3.1 RapidMiner 154 8.3.2 Weka 154 8.3.3 Keel 155 8.3.4 MO-Mine 157 8.4 Conclusion 157 Conclusion 159 Bibliography 161 Index 187

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Book SynopsisThis book celebrates Michael Stonebraker's accomplishments that led to his 2014 ACM A.M. Turing Award "for fundamental contributions to the concepts and practices underlying modern database systems."The book describes, for the broad computing community, the unique nature, significance, and impact of Mike's achievements in advancing modern database systems over more than forty years. Today, data is considered the world's most valuable resource, whether it is in the tens of millions of databases used to manage the world's businesses and governments, in the billions of databases in our smartphones and watches, or residing elsewhere, as yet unmanaged, awaiting the elusive next generation of database systems. Every one of the millions or billions of databases includes features that are celebrated by the 2014 Turing Award and are described in this book.Why should I care about databases? What is a database? What is data management? What is a database management system (DBMS)? These are just some of the questions that this book answers, in describing the development of data management through the achievements of Mike Stonebraker and his over 200 collaborators. In reading the stories in this book, you will discover core data management concepts that were developed over the two greatest eras (so far) of data management technology.The book is a collection of 36 stories written by Mike and 38 of his collaborators: 23 world-leading database researchers, 11 world-class systems engineers, and 4 business partners. If you are an aspiring researcher, engineer, or entrepreneur you might read these stories to find these turning points as practice to tilt at your own computer-science windmills, to spur yourself to your next step of innovation and achievement.Table of Contents Data Management Technology Kairometer: The Historical Context Foreword Preface Introduction PART I 2014 ACM A.M. TURING AWARD PAPER AND LECTURE The Land Sharks Are on the Squawk Box PART II MIKE STONEBRAKER'S CAREER 1. Make it Happen: The Life of Michael Stonebraker PART III MIKE STONEBRAKER SPEAKS OUT: AN INTERVIEW WITH MARIANNE WINSLETT 2. Mike Stonebraker Speaks Out: An Interview PART IV THE BIG PICTURE 3. Leadership and Advocacy 4. Perspectives: The 2014 ACM Turing Award 5. Birth of an Industry: Path to the Turing Award 6. A Perspective of Mike from a 50-Year Vantage Point PART V STARTUPS 7. How to Start a Company in Five (Not So) Easy Steps 8. How to Create and Run a Stonebraker Startup-- The Real Story 9. Getting Grownups in the Room: A VC Perspective PART VI DATABASE SYSTEMS RESEARCH 10. Where Good Ideas Come From and How to Exploit Them 11. Where We Have Failed 12. Stonebraker and Open Source 13. The Relational Database Management Systems Genealogy PART VII CONTRIBUTIONS BY SYSTEM 14. Research Contributions of Mike Stonebraker: An Overview PART VII.A RESEARCH CONTRIBUTIONS BY SYSTEM 15. The Later Ingres Years 16. Looking Back at Postgres 17. Databases Meet the Stream Processing Era 18. C-Store: Through the Eyes of a Ph.D. Student 19. In-Memory, Horizontal, and Transactional: The H-Store OLTP DBMS Project 20. Scaling Mountains: SciDB and Scientific Data Management 21. Data Unification at Scale: Data Tamer 22. The BigDAWG Polystore System 23. Data Civilizer: End-to-End Support for Data Discovery, Integration, and Cleaning PART VII.B CONTRIBUTIONS FROM BUILDING SYSTEMS 24. The Commercial Ingres Codeline 25. The Postgres and Illustra Codelines 26. The Aurora/Borealis/SteamBase Codelines: A Tale of Three Systems 27. The Vertica Codeline 28. The VoltDB Codeline 29. The SciDB Codeline: Crossing the Chasm 30. The Tamr Codeline 31. The BigDAWG Codeline PART VIII PERSPECTIVES 32. IBM Relational Database Code Bases 33. Aurum: A Story about Research Taste 34. Nice: Or What It Was Like to Be Mike's Student 35. Michael Stonebraker: Competitor, Collaborator, Friend 36. The Changing of the Database Guard PART IX SEMINAL WORKS OF MICHAEL STONEBRAKER AND HIS COLLABORATORS OTLP Through the Looking Glass, and What We Found There ""One Size Fits All"": An Idea Whose Time Has Come and Gone The End of an Architectural Era (It's Time for a Complete Rewrite) C-Store: A Column-Oriented DBMS The Implementation of POSTGRES The Design and Implementation of INGRES The Collected Works of Michael Stonebraker References Index Biographies

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Book SynopsisThis book celebrates Michael Stonebraker's accomplishments that led to his 2014 ACM A.M. Turing Award "for fundamental contributions to the concepts and practices underlying modern database systems."The book describes, for the broad computing community, the unique nature, significance, and impact of Mike's achievements in advancing modern database systems over more than forty years. Today, data is considered the world's most valuable resource, whether it is in the tens of millions of databases used to manage the world's businesses and governments, in the billions of databases in our smartphones and watches, or residing elsewhere, as yet unmanaged, awaiting the elusive next generation of database systems. Every one of the millions or billions of databases includes features that are celebrated by the 2014 Turing Award and are described in this book.Why should I care about databases? What is a database? What is data management? What is a database management system (DBMS)? These are just some of the questions that this book answers, in describing the development of data management through the achievements of Mike Stonebraker and his over 200 collaborators. In reading the stories in this book, you will discover core data management concepts that were developed over the two greatest eras (so far) of data management technology.The book is a collection of 36 stories written by Mike and 38 of his collaborators: 23 world-leading database researchers, 11 world-class systems engineers, and 4 business partners. If you are an aspiring researcher, engineer, or entrepreneur you might read these stories to find these turning points as practice to tilt at your own computer-science windmills, to spur yourself to your next step of innovation and achievement.Table of Contents Data Management Technology Kairometer: The Historical Context Foreword Preface Introduction PART I 2014 ACM A.M. TURING AWARD PAPER AND LECTURE The Land Sharks Are on the Squawk Box PART II MIKE STONEBRAKER'S CAREER 1. Make it Happen: The Life of Michael Stonebraker PART III MIKE STONEBRAKER SPEAKS OUT: AN INTERVIEW WITH MARIANNE WINSLETT 2. Mike Stonebraker Speaks Out: An Interview PART IV THE BIG PICTURE 3. Leadership and Advocacy 4. Perspectives: The 2014 ACM Turing Award 5. Birth of an Industry: Path to the Turing Award 6. A Perspective of Mike from a 50-Year Vantage Point PART V STARTUPS 7. How to Start a Company in Five (Not So) Easy Steps 8. How to Create and Run a Stonebraker Startup-- The Real Story 9. Getting Grownups in the Room: A VC Perspective PART VI DATABASE SYSTEMS RESEARCH 10. Where Good Ideas Come From and How to Exploit Them 11. Where We Have Failed 12. Stonebraker and Open Source 13. The Relational Database Management Systems Genealogy PART VII CONTRIBUTIONS BY SYSTEM 14. Research Contributions of Mike Stonebraker: An Overview PART VII.A RESEARCH CONTRIBUTIONS BY SYSTEM 15. The Later Ingres Years 16. Looking Back at Postgres 17. Databases Meet the Stream Processing Era 18. C-Store: Through the Eyes of a Ph.D. Student 19. In-Memory, Horizontal, and Transactional: The H-Store OLTP DBMS Project 20. Scaling Mountains: SciDB and Scientific Data Management 21. Data Unification at Scale: Data Tamer 22. The BigDAWG Polystore System 23. Data Civilizer: End-to-End Support for Data Discovery, Integration, and Cleaning PART VII.B CONTRIBUTIONS FROM BUILDING SYSTEMS 24. The Commercial Ingres Codeline 25. The Postgres and Illustra Codelines 26. The Aurora/Borealis/SteamBase Codelines: A Tale of Three Systems 27. The Vertica Codeline 28. The VoltDB Codeline 29. The SciDB Codeline: Crossing the Chasm 30. The Tamr Codeline 31. The BigDAWG Codeline PART VIII PERSPECTIVES 32. IBM Relational Database Code Bases 33. Aurum: A Story about Research Taste 34. Nice: Or What It Was Like to Be Mike's Student 35. Michael Stonebraker: Competitor, Collaborator, Friend 36. The Changing of the Database Guard PART IX SEMINAL WORKS OF MICHAEL STONEBRAKER AND HIS COLLABORATORS OTLP Through the Looking Glass, and What We Found There ""One Size Fits All"": An Idea Whose Time Has Come and Gone The End of an Architectural Era (It's Time for a Complete Rewrite) C-Store: A Column-Oriented DBMS The Implementation of POSTGRES The Design and Implementation of INGRES The Collected Works of Michael Stonebraker References Index Biographies

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Book SynopsisRecent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic.This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.Table of Contents PART I. OVERVIEW AND BACKGROUND Introduction Background Text Data Understanding MeTA: A Unified Toolkit for Text Data Management and Analysis PART II. TEXT DATA ACCESS Overview of Text Data Access Retrieval Models Feedback Search Engine Implementation Search Engine Evaluation Web Search Recommender Systems PART III. TEXT DATA ANALYSIS Overview of Text Data Analysis Word Association Mining Text Clustering Text Categorization Text Summarization Topic Analysis Opinion Mining and Sentiment Analysis PART IV. UNIFIED TEXT DATA MANAGEMENT ANALYSIS SYSTEM Toward a Unified System for Text Management and Analysis Appendix A. Bayesian Statistics Appendix B. Expectation-Maximization Appendix C. KL-divergence and Dirichlet Prior Smoothing References Index Authors Biographies

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Book SynopsisRecent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic.This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.Table of Contents PART I. OVERVIEW AND BACKGROUND Introduction Background Text Data Understanding MeTA: A Unified Toolkit for Text Data Management and Analysis PART II. TEXT DATA ACCESS Overview of Text Data Access Retrieval Models Feedback Search Engine Implementation Search Engine Evaluation Web Search Recommender Systems PART III. TEXT DATA ANALYSIS Overview of Text Data Analysis Word Association Mining Text Clustering Text Categorization Text Summarization Topic Analysis Opinion Mining and Sentiment Analysis PART IV. UNIFIED TEXT DATA MANAGEMENT ANALYSIS SYSTEM Toward a Unified System for Text Management and Analysis App. A. Bayesian Statistics App. B. Expectation-Maximization App. C. KL-divergence and Dirichlet Prior Smoothing References Index Authors Biographies

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Book SynopsisCovering all the essential components of Unix/Linux, including process management, concurrent programming, timer and time service, file systems and network programming, this textbook emphasizes programming practice in the Unix/Linux environment. Systems Programming in Unix/Linux is intended as a textbook for systems programming courses in technically-oriented Computer Science/Engineering curricula that emphasize both theory and programming practice. The book contains many detailed working example programs with complete source code. It is also suitable for self-study by advanced programmers and computer enthusiasts.Systems programming is an indispensable part of Computer Science/Engineering education. After taking an introductory programming course, this book is meant to further knowledge by detailing how dynamic data structures are used in practice, using programming exercises and programming projects on such topics as C structures, pointers, link lists and trees.This book provides a wide range of knowledge about computer systemsoftware and advanced programming skills, allowing readers to interface with operatingsystem kernel, make efficient use of system resources and develop application software.It also prepares readers with the needed background to pursue advanced studies inComputer Science/Engineering, such as operating systems, embedded systems, databasesystems, data mining, artificial intelligence, computer networks, network security,distributed and parallel computing.Table of ContentsChapter 1. Introduction to Unix/Linux.- Chapter 2. Programming Background.- Chapter 3 Process Management in Unix/Linux.- Chapter 4 Concurrent Programming.- Chapter 5 Timers and Time Service.- Chapter 6 Signals and Signal Processing.- Chapter 7 File Operations.- Chapter 8 System Calls for File Operations.- Chapter 9 Library I/O FunctionsChapter 10 Sh Programming.- Chapter 11 EXT2 File System.- Chapter 12. Block Device I/O and Buffer Management.- Chapter 13 TCP/IP and Network Programming.

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Book SynopsisThis book offers an essential introduction to a new urban planning and design methodology called Data Augmented Design (DAD) and its evolution and progresses, highlighting data driven methods, urban planning and design applications and related theories. The authors draw on many kinds of data, including big, open, and conventional data, and discuss cutting-edge technologies that illustrate DAD as a future oriented design framework in terms of its focus on multi-data, multi-method, multi-stage and multi-scale sustainable urban planning. In four sections and ten chapters, the book presents case studies to address the core concepts of DAD, the first type of applications of DAD that emerged in redevelopment-oriented planning and design, the second type committed to the planning and design for urban expansion, and the future-oriented applications of DAD to advance sustainable technologies and the future structural form of the built environment. The book is geared towards a broad readership, ranging from researchers and students of urban planning, urban design, urban geography, urban economics, and urban sociology, to practitioners in the areas of urban planning and design.​ Table of ContentsChapter 1. Cities in Transition. - Chapter 2. Data Augmented Design (DAD): Definitions, Dimensions, Performance, and Applications. - Chapter 3. Human-scale Urban Form and its Application in DAD. - Chapter 4. Data Adaptive Urban Design: A Case Study of Shanghai Hengfu Historical District. - Chapter 5. Multidimensional Data-based City Images: Cultural Reactivation of Waterfront Industrial Heritage Design in Shanghai. - Chapter 6. Fine-Scale Recognition-based Design Guidelines for Dealing with Shrinking Cities: A Case Study of Hegang. - Chapter 7. Quantifying Urban Form as a Case Study in Expansion-oriented Design: Design Practices in the Tongzhou Subcenter. - Chapter 8. Defining the Density of the Xiong’an New Area based on Global Experience. - Chapter 9. The Next Form of Human Settlement: A Design for Future Yilong City. - Chapter 10. The Future of the Smart Island: A Design for a Natural and Technological Experience District on Huangguan Island.

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Book SynopsisThis book presents a unique, step-by-step approach for monitoring, detecting, analyzing and mitigating complex network cyber threats. It includes updated processes in response to asymmetric threats, as well as descriptions of the current tools to mitigate cyber threats. Featuring comprehensive computer science material relating to a complete network baseline with the characterization hardware and software configuration, the book also identifies potential emerging cyber threats and the vulnerabilities of the network architecture to provide students with a guide to responding to threats. The book is intended for undergraduate and graduate college students who are unfamiliar with the cyber paradigm and processes in responding to attacks. Table of ContentsPre-incident Planning and Analysis.- Incident Detection and Characterization.- Vulnerability/Consequence Analysis.- Incident Response and Recovery.- Cloud Architecture.- Lessons Learned.

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Book SynopsisThis book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society.Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.Table of ContentsStudy on Discrete Action Sequences using Deep Emotional Intelligence.- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms.- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm.- Knowledge Framework for Deep Learning: Congenital Heart Disease.- Computing System and Machine Learning.- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.

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Book SynopsisThis book is an introductory text on design science, intended to support both graduate students and researchers in structuring, undertaking and presenting design science work. It builds on established design science methods as well as recent work on presenting design science studies and ethical principles for design science, and also offers novel instruments for visualizing the results, both in the form of process diagrams and through a canvas format. While the book does not presume any prior knowledge of design science, it provides readers with a thorough understanding of the subject and enables them to delve into much deeper detail, thanks to extensive sections on further reading. Design science in information systems and technology aims to create novel artifacts in the form of models, methods, and systems that support people in developing, using and maintaining IT solutions. This work focuses on design science as applied to information systems and technology, but it also includes examples from, and perspectives of, other fields of human practice. Chapter 1 provides an overview of design science and outlines its ties with empirical research. Chapter 2 discusses the various types and forms of knowledge that can be used and produced by design science research, while Chapter 3 presents a brief overview of common empirical research strategies and methods. Chapter 4 introduces a methodological framework for supporting researchers in doing design science research as well as in presenting their results. This framework includes five core activities, which are described in detail in Chapters 5 to 9. Chapter 10 discusses how to communicate design science results, while Chapter 11 compares the proposed methodological framework with methods for systems development and shows how they can be combined. Chapter 12 discusses how design science relates to research paradigms, in particular to positivism and interpretivism, and Chapter 13 discusses ethical issues and principles for design science research. The new Chapter 14 showcases a study on digital health consultations and illustrates the whole process in one comprehensive example. Also added to this 2nd edition are a number of sections on practical guidelines for carrying out basic design science tasks, a discussion on design thinking and its relationship to design science, and the description of artefact classifications. Eventually, both the references in each chapter and the companion web site were updated to reflect recent findings.Table of Contents1 Introduction.- 2 Knowledge Types and Forms.- 3 Research Strategies and Methods.- 4 A Method Framework for Design Science Research.- 5 Explicate Problem.- 6 Define Requirements.- 7 Design and Develop Artefact.- 8 Demonstrate Artefact.- 9 Evaluate Artefact.- 10 Communicate Artefact Knowledge.- 11 Systems Development and the Method Framework for Design Science Research.- 12 Research Paradigms.- 13 Ethics and Design Science. 14 Digital Consultations — a Case Study.

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Book SynopsisThis book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users. 1. Highlights recent applications and techniques in cyber intelligence2. Includes the proceedings of the 2021 International Conference on Applications and Techniques in Cyber Intelligence (ATCI 2021) 3. Presents a broad range of scientific research on cyber intelligence

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Book SynopsisThis book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users.

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Book SynopsisThis book briefly covers internationally contributed chapters with artificial intelligence and applied mathematics-oriented background-details. Nowadays, the world is under attack of intelligent systems covering all fields to make them practical and meaningful for humans. In this sense, this edited book provides the most recent research on use of engineering capabilities for developing intelligent systems. The chapters are a collection from the works presented at the 2nd International Conference on Artificial Intelligence and Applied Mathematics in Engineering held within 09-10-11 October 2020 at the Antalya, Manavgat (Turkey). The target audience of the book covers scientists, experts, M.Sc. and Ph.D. students, post-docs, and anyone interested in intelligent systems and their usage in different problem domains. The book is suitable to be used as a reference work in the courses associated with artificial intelligence and applied mathematics. Table of ContentsPrediction of Liver Cancer by Artificial Neural Network.- Remarks on the limit-circle classification of Conformable Fractional Sturm-Liouville Operator.- Improving Search Relevance with Word Embedding Based Clusters.- Improving Search Relevance with Word Embedding Based Clusters.- Diagnosis of Parkinson's Disease with Acoustic Sounds by Rule Based Model.- Development of Face Recognition System by Using Deep Learning and FaceNet Algorithm in the Operations Processes.- Mobile Assisted Travel Planning Software: The Case of Burdur.- Optimal Coordination of Directional Overcurrent Relays Using Artificial Ecosystem-based Optimization.- The Effect of Auscultation Areas on Nonlinear Classifiers in Computerized Analysis of Chronic Obstructive Pulmonary Disease.

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Book SynopsisThis book includes proceedings of the 15th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2021), which took place in Asan, Korea, on July 1-3, 2021. With the proliferation of wireless technologies and electronic devices, there is a fast-growing interest in Ubiquitous and Pervasive Computing (UPC). The UPC enables to create a human-oriented computing environment where computer chips are embedded in everyday objects and interact with physical world. Through UPC, people can get online even while moving around, thus, having almost permanent access to their preferred services. With a great potential to revolutionize our lives, UPC also poses new research challenges.The aim of the book is to provide the latest research findings, methods, development techniques, challenges, and solutions from both theoretical and practical perspectives related to UPC with an emphasis on innovative, mobile, and Internet services.

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Book SynopsisThis book comprises high-quality refereed research papers presented at The Second International Symposium on Computer Science, Digital Economy and Intelligent Systems (CSDEIS2020), held in Moscow, Russia, on December 18–20, 2020, organized jointly by Moscow State Technical University and the International Research Association of Modern Education and Computer Science. The topics discussed in the book include state-of-the-art papers in computer science and their technological applications; intelligent systems and intellectual approaches; digital economics and methodological approaches. It is an excellent source of references for researchers, graduate students, engineers, management practitioners, and undergraduate students interested in computer science and their applications in engineering and management.Table of ContentsOn Mixed Forced and Self-oscillations with Delays in Elasticity and Friction.- An Extensible Network Traffic Classifier Based on Machine Learning Methods.- Intelligent Information Systems Based on Notional Models without Relationships.- Study of Properties of Growing Random Graphs with Neuron-like Structure.- Planning of Computational Experiments in Verification of Mathematical Models of Dynamic Machine Systems.- Optimization of Network Transmission of Multimedia Data Stream in a Cloud System.

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Book SynopsisThis book contains the proceedings of the 22nd EANN “Engineering Applications of Neural Networks” 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent – long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.Table of ContentsAutomatic Facial Expression Neutralisation Using Generative Adversarial Network.- Creating Ensembles of Generative Adversarial Network Discriminators for One-class Classification.- A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection.- Anomaly Detection by Robust Feature Reconstruction.- Deep Learning of Brain Asymmetry Images and Transfer Learning for Early Diagnosis of Dementia.- Deep learning topology-preserving EEG-based images for autism detection in infants.- Improving the Diagnosis of Breast Cancer by Combining Visual and Semantic Feature Descriptors.- Liver cancer trait detection and classification through Machine Learning on smart mobile devices.

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Book SynopsisThis book describes how to use expert knowledge—which is often formulated by using imprecise (fuzzy) words from a natural language. In the 1960s, Zadeh designed special "fuzzy" techniques for such use. In the 1980s, fuzzy techniques started controlling trains, elevators, video cameras, rice cookers, car transmissions, etc. Now, combining fuzzy with neural, genetic, and other intelligent methods leads to new state-of-the-art results: in aerospace industry (from drones to space flights), in mobile robotics, in finances (predicting the value of crypto-currencies), and even in law enforcement (detecting counterfeit banknotes, detecting online child predators and in creating explainable AI systems). The book describes these (and other) applications—as well as foundations and logistics of fuzzy techniques. This book can be recommended to specialists—both in fuzzy and in various application areas—who will learn latest techniques and their applications, and to students interested in innovative ideas.Table of ContentsPowerset operators in categories with fuzzy relations dened by monads.- Improved Fuzzy Q-Learning with Replay Memory.- The ulem package: underlining for emphasis.- A Dynamic Hierarchical Genetic-Fuzzy Sugeno Network.- Fuzzy Mathematical Morphology and Applications in Image Processing.

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Book SynopsisThis book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques. This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI—in short, to anyone who is interested in problems involving fuzziness and AI in general.

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Book SynopsisThis book describes the utilization of different soft computing techniques and their optimization for providing an accurate and efficient medical diagnosis. The proposed method provides a precise and timely diagnosis of the risk that a person has to develop a particular disease, but it can be adaptable to provide the diagnosis of different diseases. This book reflects the experimentation that was carried out, based on the different optimizations using bio-inspired algorithms (such as bird swarm algorithm, flower pollination algorithms, and others). In particular, the optimizations were carried out to design the fuzzy classifiers of the nocturnal blood pressure profile and heart rate level. In addition, to obtain the architecture that provides the best result, the neurons and the number of neurons per layers of the artificial neural networks used in the model are optimized. Furthermore, different tests were carried out with the complete optimized model. Another work that is presented in this book is the dynamic parameter adaptation of the bird swarm algorithm using fuzzy inference systems, with the aim of improving its performance. For this, different experiments are carried out, where mathematical functions and a monolithic neural network are optimized to compare the results obtained with the original algorithm. The book will be of interest for graduate students of engineering and medicine, as well as researchers and professors aiming at proposing and developing new intelligent models for medical diagnosis. In addition, it also will be of interest for people working on metaheuristic algorithms and their applications on medicine.

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Book SynopsisThis edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.Table of ContentsEngagement Analysis of Students in Online Learning Environments.- Application of Artificial Intelligence to predict the Degradation of Potential mRNA Vaccines Developed To Treat SARS-CoV-2.- An Application of Transfer Learning: Fine-Tuning BERT for Spam Email Classification.- MMAP : A Multi-Modal Automated Online Proctor.- Applying Extreme Gradient Boosting for Surface EMG based Sign Language recognition.- Review of Security Aspects of 51 Percent Attack on Blockchain.- Integrated Micro-video Recommender based on Hadoop and Web-Scrapper.- Automated Sleep Staging System based on Ensemble Learning Model using Single-Channel EEG signal.- Segregation and User Interactive Visualization of Covid- 19 Tweets using Text Mining Techniques.- Software Fault Prediction using Data Mining Techniques on Software Metrics.

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Book SynopsisThis book presents the general objective of the REV2021 conference which is to contribute and discuss fundamentals, applications, and experiences in the field of Online and Remote Engineering, Virtual Instrumentation, and other related new technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber Security, and M2M & Smart Objects. Nowadays, online technologies are the core of most fields of engineering and the whole society and are inseparably connected, for example, with Internet of Things, Industry 4.0 & Industrial Internet of Things, Cloud Technologies, Data Science, Cross & Mixed Reality, Remote Working Environments, Online & Biomedical Engineering, to name only a few.Since the first REV conference in 2004, we tried to focus on the upcoming use of the Internet for engineering tasks and the opportunities as well as challenges around it. In a globally connected world, the interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. Another objective of the conference is to discuss guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and Open Resources.REV2021 on "Online Engineering and Society 4.0" was the 17th in a series of annual events concerning the area of Remote Engineering and Virtual Instrumentation. It has been organized in cooperation with the International Engineering and Technology Institute (IETI) as an online event from February 24 to 26, 2021.Table of ContentsOn the Development of a Unified Remote Laboratory Framework.- GOLDi 2.0: Beyond Raw Digital Signals – Electrical Interface Emulation.- A Reliable Real-time Web Interface for an Online Laboratory.- Remote Labs For Communications.- Automated Testing for Sustainable Remote Laboratory System.- Interactive Lab Experimentation And Simulation Tools For Remote Laboratories.- Aligning Technic with Didactic – A Remote Laboratory Infrastructure for Study, Teaching and Research.- Simulation on Motion of A Trebuchet.- Human-Centered Design in Online Laboratories for Graduate Engineering Students.

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Book SynopsisThis book presents findings from the papers accepted at the Cyber Security Education Stream and Cyber Security Technology Stream of The National Cyber Summit’s Research Track, reporting on latest advances on topics ranging from software security to cyber-attack detection and modelling to the use of machine learning in cyber security to legislation and policy to surveying of small businesses to cyber competition, and so on. Understanding the latest capabilities in cyber security ensures users and organizations are best prepared for potential negative events. This book is of interest to cyber security researchers, educators and practitioners, as well as students seeking to learn about cyber security.Table of ContentsPart I – Cyber Security EducationAn Integrated System for Connecting Cybersecurity Competency, Student Activities and Career Building Li-Chiou Chen, Andreea Cotoranu, Praviin Mandhare and Darren Hayes Simulating Industrial Control Systems using Node-RED and Unreal Engine 4 Steven Day, William Smallwood and Joshua Kuhn Student Educational Learning Experience Through Cooperative Research Melissa Hannis, Idongesit Mkpong-Ruffin and Drew Hamilton Digital Forensics Education: Challenges and Future Opportunities Megan Stigall and Kim-Kwang Raymond Choo Designing a Cybersecurity Curriculum Library: Best Practices from Digital Library Research Blair Taylor, Sidd Kaza and Melissa Dark Design of a Virtual Cybersecurity Escape Room Tania Williams and Omar El-Gayar Part II – Cyber Security Technology A Novel Method for the Automated Generation for JOP Chain Exploits Bramwell Brizendine, Austin Babcock and Josh Stroschien Increasing Log Availability in Unmanned Vehicle Systems Nicholas Carter, Peter Pommer, Duane Davis and Cynthia Irvine Testing Detection of K-Ary Code Obfuscated by Metamorphic and Polymorphic Techniques George Harter and Neil Rowe Enhancing Secure Coding Assistant System with Design by Contract and Programming Logic Wenhui Liang, Cui Zhang and Jun Dai Social Engineering Attacks in Healthcare Systems: A Survey Christopher Nguyen, Walt Williams, Brandon Didlake, Donte Mitchell, James McGinnis and Dipankar Dasgupta Identifying Anomalous Industrial-Control-System Network Flow Activity Using Cloud Honeypots Neil Rowe, Thuy Nguyen, Jeffrey Dougherty, Matthew Bieker and Darry Pilkington Risks of Electric Vehicle Supply Equipment Integration within Building Energy Management System Environments: A Look at Remote Attack Surface and Implications Roland Varriale, Michael Jaynes and Ryan Crawford

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Book SynopsisThis book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., “mechanistic” principles) to solve intractable problems. Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here in six easy-to-follow modules: 1) Multimodal data generation and collection, 2) extraction of mechanistic features, 3) knowledge-driven dimension reduction, 4) reduced order surrogate models, 5) deep learning for regression and classification, and 6) system and design. These data science and mechanistic analysis steps are presented in an intuitive manner that emphasizes practical concepts for solving engineering problems as well as real-life problems. This book is written in a spectral style and is ideal as an entry level textbook for engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM (Science, Technology, Engineering, Mathematics) high school students and teachers.Table of Contents1-Introduction to Mechanistic Data Science 2-Multimodal Data Generation and Collection 3-Optimization and Regression 4-Extraction of Mechanistic Features 5-Knowledge-Driven Dimension Reduction and Reduced Order Surrogate Models 6-Deep Learning for Regression and Classification 7-System and Design

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Book SynopsisThis book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field. Table of ContentsAnalysis of Sentiment Tendency of Tourists' Comments Based on Text Mining.- Analysis of Smart City Construction Based on 5G Data Technology.- Prediction of Stock Price Based on Artificial Intelligence Algorithm.- Variation Translation Strategy System of Intangible Cultural Heritage Based on Data Mining.- A Computer-aided Comparative Study on Grammatical Cohesion in Abstracts of Sci-tech Journal Papers by Chinese and American Scholars.- Computer Graphics and Image Software in Advertising Design.- Design and Research of Production Information Management System for Project Based Mechanical Manufacturing Enterprises.- Impact of Computer Network Technology on Regional Economic Development.- Chaos Algorithm of Electrical Control System Based on Neural Network Technology.- Pulse Signal Acquisition System Based on Match Pursuit Algorithm.- Data Analysis of Power System Engineering Construction Based on PPSO Algorithm.- Reactive Optimization of Power System Based on K-means Algorithm.- Design and Structure Analysis of Manipulator based on Acceleration Sensor.- Discussion on Decision Tree Algorithm in University Teaching Management System.

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Book SynopsisThis book explores the benefits to online teaching incorporating extended reality technologies both from a teacher’s and from a students’ perspective. As we are all aware, the COVID-19 pandemic has created a worldwide lock down which is clearly visible in individuals’ shifting behaviour as they are keeping away from public contact, large events, weddings, places of worship, public transportation, restaurant, flights, shopping malls, etc. People across the world have adopted to Work From Home (WFH) concept using digital technology. They are teaching, learning, conducting meetings, seminars, etc., using digital medium. As people were not allowed to go out and buy things, online shopping was in demand and extensible reality helped in marketing the products and customers could also have a better shopping experience. Gaming industry has always brought in many new games for children and adults. Healthcare sector also leveraged the benefits of this technology to the fullest extent. The use of augmented and virtual reality in art and museum is also highlighted. Our book presents the different sectors that have benefitted using this technology during this time of crisis. This book will be very useful for students, professionals and researchers working in the area of virtual, augmented or mixed reality. Our aim is to bring out the use of this technology during the COVID-19 pandemic so that the readers are exposed to the various applications of this technology.Table of Contents1. Use of Extended Reality in Medicine during the Covid-19 pandemic.- XR based remote learning experience during pandemic: Effectiveness and Barriers.- How virtual and augmented reality are reshaping the fashion industry during the Covid-19 Pandemic.

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Book SynopsisThis book gathers the proceedings of the International Conference on Information, Communication and Cybersecurity, held on November 10–11, 2021, in Khouribga, Morocco. The conference was jointly coorganized by The National School of Applied Sciences of Sultan Moulay Slimane University, Morocco, and Charles Darwin University, Australia. This book provides an opportunity to account for state-of-the-art works, future trends impacting information technology, communications, and cybersecurity, focusing on elucidating the challenges, opportunities, and inter-dependencies that are just around the corner. This book is helpful for students and researchers as well as practitioners.ICI2C 2021 was devoted to advances in smart information technologies, communication, and cybersecurity. It was considered a meeting point for researchers and practitioners to implement advanced information technologies into various industries. There were 159 paper submissions from 24 countries. Each submission was reviewed by at least three chairs or PC members. We accepted 54 regular papers (34\%). Unfortunately, due to limitations of conference topics and edited volumes, the Program Committee was forced to reject some interesting papers, which did not satisfy these topics or publisher requirements. We would like to thank all authors and reviewers for their work and valuable contributions. The friendly and welcoming attitude of conference supporters and contributors made this event a success!

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Book SynopsisThis book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.Table of ContentsChapter 1: An Optimization of Fuzzy Rough Set Nearest Neighbor Classification Model using Krill Herd Algorithm for Sentiment Text Analytics.- Chapter 2: Fuzzy Wavelet Neural Network with Social Spider Optimization Algorithm for Pattern Recognition in Medical Domain.- Chapter 3: Fuzzy with Gravitational Search Algorithm Tuned Radial Basis Function Network for Medical Disease Diagnosis and Classification Model.- Chapter 4: Optimal Neutrosophic Rules based Feature Extraction for Data Classification using Deep Learning Model.- Chapter 5: Self-Evolving Interval Type-2 Fuzzy Neural Network Design for The Synchronization of Chaotic Systems.- Chapter 6: Categorizing Relations via Semi-Supervised Learning using a Hybrid Tolerance Rough Sets and Genetic Algorithm Approach.- Chapter 7: Data-driven Fuzzy C-Means Equivalent Turbine-governor for Power System Frequency Response.- Chapter 8: Multicriteria group decision making using a novel similarity measure for triangular fuzzy numbers based on their newly defined expected values and variances.- Chapter 9: Bangla Printed Character Generation from Handwritten Character Using GAN.

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Book SynopsisThis book presents techniques and security challenges of chaotic systems and their use in cybersecurity. It presents the state-of-the-art and the latest discoveries in the field of chaotic systems and methods and proposes new models, practical solutions, and technological advances related to new chaotic dynamical systems. The book can be used as part of the bibliography of the following courses: - Cybersecurity - Cryptography - Networks and Communications Security - Nonlinear Circuits - Nonlinear Systems and ApplicationsTable of ContentsA novel approach for robust S-box construction using a 5-D chaotic map and its application to image cryptosystem.- An image compression-encryption algorithm based on compressed sensing and chaotic oscillator.- Backstepping and sliding mode control of a fractional-order chaotic system.- Quantum oscillations: A promising field for secure communication.- Synchronization of chaotic electroencephalography (EEG) signals.- Secure Communication Scheme Based on Hyperchaotic Systems.

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