Databases / Data management Books

Book SynopsisData warehousing is split into two camps: Ralph Kimball leads those who champion a technique called dimensional modeling; Bill Inmon leads the rest who believe in using relational modeling techniques.Table of ContentsAcknowledgments. About the Authors. PART ONE: CONCEPTS. Chapter 1. Introduction. Chapter 2. Fundamental Relational Concepts. PART TWO: MODEL DEVELOPMENT. Chapter 3. Understanding the Business Model. Chapter 4. Developing the Model. Chapter 5. Creating and Maintaining Keys. Chapter 6. Modeling the Calendar. Chapter 7. Modeling Hierarchies. Chapter 8. Modeling Transactions. Chapter 9. Data Warehouse Optimization. PART THREE: OPERATION AND MANAGEMENT. Chapter 10. Accommodating Business Change. Chapter 11. Maintaining the Models. Chapter 12. Deploying the Relational Solution. Chapter 13. Comparison of Data Warehouse Methodologies. Glossary. Recommended Reading. Index.

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Book SynopsisThe many and powerful data structures for representing information physically (in contrast to a database management system that represents information with logical structures) are introduced by this book.Table of ContentsPreface xi Part One Primary File Organizations 25 Part Two Bit Level And Related Structures 127 Part Three Tree Structures 197 Part Four File Sorting 337 Answers to Selected Exercises 375 Index 393

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Book SynopsisTrade Review"[A] penetrating study of missing (‘dark’) data and its impacts on decisions—skewing stats, enabling fraud, embedding inequity and triggering preventable catastrophes. Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy."---Barbara Kiser, Nature"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. He writes in an accessible and understandable way too."---Simon Cocking, Irish Tech News"Well-written and accessible."---Tim Harford, Undercover Economist"You need to read [Dark Data], and be convinced by David’s reasoning and his examples of cases in which unseen or unreported data play a critical and sometimes even a fatal role. You are likely to walk away with the feeling that the term dark data is indeed a very effective one to arouse both curiosity and suspicion, mixed with happiness that finally a great term was coined by a statistician—and sadness that the statistician is not you."---Xiao-Li Meng, IMS Bulletin"An exploration of a major problem in data analysis with an attempt of classification, analysing causes, mechanisms, and to some extent also suggest mitigations."---Adhemar Bultheel, European Mathematical Society"An excellent guide to the many reasons for caution in interpreting data."---Diane Coyle, Enlightened Economist

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Book SynopsisTrade Review"[A] penetrating study of missing (‘dark’) data and its impacts on decisions—skewing stats, enabling fraud, embedding inequity and triggering preventable catastrophes. Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy."---Barbara Kiser, Nature"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. He writes in an accessible and understandable way too."---Simon Cocking, Irish Tech News"Well-written and accessible."---Tim Harford, Undercover Economist"You need to read [Dark Data], and be convinced by David’s reasoning and his examples of cases in which unseen or unreported data play a critical and sometimes even a fatal role. You are likely to walk away with the feeling that the term dark data is indeed a very effective one to arouse both curiosity and suspicion, mixed with happiness that finally a great term was coined by a statistician—and sadness that the statistician is not you."---Xiao-Li Meng, IMS Bulletin"An exploration of a major problem in data analysis with an attempt of classification, analysing causes, mechanisms, and to some extent also suggest mitigations."---Adhemar Bultheel, European Mathematical Society"An excellent guide to the many reasons for caution in interpreting data."---Diane Coyle, Enlightened Economist

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Book SynopsisThis comprehensive guide offers a detailed treatment of the analysis, design, simulation and testing of the full range of today''s leading delta-sigma data converters. Written by professionals experienced in all practical aspects of delta-sigma modulator design, Delta-Sigma Data Converters provides comprehensive coverage of low and high-order single-bit, bandpass, continuous-time, multi-stage modulators as well as advanced topics, including idle-channel tones, stability, decimation and interpolation filter design, and simulation.Table of ContentsPreface. Introduction. An Overview of Basic Concepts (J. Candy). Quantization Noise in DeltaSigma A/D Converters (R. Gray). Quantization Errors and Dithering in DeltaSigma Modulators (S. Norsworthy). Stability Theory for DeltaSigma Modulators (R. Adams & R. Schreier). The Design of High-Order Single-Bit DeltaSigma ADCs (R. Adams). The Design of Cascaded DeltaSigma ADCs (M. Rebeschini). High-Speed Cascaded DeltaSigma ADCs (B. Brandt). Delta-Sigma ADCs with Multibit Internal Converters (R. Carley, et al.). The Design of Bandpass DeltaSigma ADCs (S. Jantzi, et al.). Architectures for DeltaSigma DACs (G. Temes, et al.). Analog Circuit Design for DeltaSigma ADCs (B. Brandt, et al.). Analog Circuit Design for DeltaSigma DACs (M. Rebeschini & P. Ferguson). Decimation and Interpolation for DeltaSigma Conversion (S. Norsworthy & R. Crochiere). CAD for the Analysis and Design of DeltaSigma Converters (C. Wolff, et al.). Index. About the Editors.

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Book SynopsisTable of ContentsPreface. Introduction to Data Conversion and Processing. Basic Sampling Circuits. Sample-and-Hold Architectures. Basic Principles of Digital-to-Analog Conversion. Digital-to-Analog Converter Architectures. Analog-to-Digital Converter Architectures. Building Blocks Data Conversion Systems. Precision Techniques. Testing and Characterization. Index.

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Book SynopsisThis book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.Table of ContentsPreface xv Acknowledgments xix Some Notation xxi 1 Introduction 1 1.1 Big Data: Basic Concepts 1 1.2 Data Mining with Big Data 9 1.3 A Mathematical Introduction to Big Data 13 1.4 A Mathematical Theory of Big Data 28 1.5 Smart Grid 34 1.6 Big Data and Smart Grid 36 1.7 Reading Guide 37 Bibliographical Remarks 39 Part I Fundamentals of Big Data 41 2 The Mathematical Foundations of Big Data Systems 43 2.1 Big Data Analytics 44 2.2 Big Data: Sense, Collect, Store, and Analyze 45 2.3 Intelligent Algorithms 48 2.4 Signal Processing for Smart Grid 48 2.5 Monitoring and Optimization for Power Grids 48 2.6 Distributed Sensing and Measurement for Power Grids 49 2.7 Real-time Analysis of Streaming Data 50 2.8 Salient Features of Big Data 51 2.9 Big Data for Quantum Systems 54 2.10 Big Data for Financial Systems 55 2.11 Big Data for Atmospheric Systems 73 2.12 Big Data for Sensing Networks 74 2.13 Big Data forWireless Networks 75 2.14 Big Data for Transportation 78 Bibliographical Remarks 78 3 Large Random Matrices: An Introduction 79 3.1 Modeling of Large Dimensional Data as Random Matrices 79 3.2 A Brief of Random MatrixTheory 81 3.3 Change Point of Views: From Vectors to Measures 85 3.4 The Stieltjes Transform of Measures 86 3.5 A Fundamental Result: The Marchenko–Pastur Equation 88 3.6 Linear Eigenvalue Statistics and Limit Laws 89 3.7 Central LimitTheorem for Linear Eigenvalue Statistics 99 3.8 Central LimitTheorem for Random Matrix S−1T 101 3.9 Independence for Random Matrices 103 3.10 Matrix-Valued Gaussian Distribution 110 3.11 Matrix-ValuedWishart Distribution 112 3.12 Moment Method 112 3.13 Stieltjes Transform Method 113 3.14 Concentration of the Spectral Measure for Large Random Matrices 114 3.15 Future Directions 117 Bibliographical Remarks 117 4 Linear Spectral Statistics of the Sample Covariance Matrix 121 4.1 Linear Spectral Statistics 121 4.2 Generalized Marchenko–Pastur Distributions 122 4.3 Estimation of Spectral Density Functions 127 4.4 Limiting Spectral Distribution of Time Series 146 Bibliographical Remarks 154 5 Large Hermitian Random Matrices and Free Random Variables 155 5.1 Large Economic/Financial Systems 156 5.2 Matrix-Valued Probability 157 5.3 Wishart-Levy Free Stable Random Matrices 166 5.4 Basic Concepts for Free Random Variables 168 5.5 The Analytical Spectrum of theWishart–Levy Random Matrix 172 5.6 Basic Properties of the Stieltjes Transform 176 5.7 Basic Theorems for the Stieltjes Transform 179 5.8 Free Probability for Hermitian Random Matrices 185 5.9 Random Vandermonde Matrix 196 5.10 Non-Asymptotic Analysis of State Estimation 200 Bibliographical Remarks 201 6 Large Non-Hermitian Random Matrices and Quatartenionic Free Probability Theory 203 6.1 Quatartenionic Free ProbabilityTheory 204 6.2 R-diagonalMatrices 209 6.3 The Sum of Non-Hermitian Random Matrices 216 6.4 The Product of Non-Hermitian Random Matrices 220 6.5 Singular Value Equivalent Models 226 6.6 The Power of the Non-Hermitian Random Matrix 234 6.7 Power Series of Large Non-Hermitian Random Matrices 239 6.8 Products of Random Ginibre Matrices 246 6.9 Products of Rectangular Gaussian Random Matrices 249 6.10 Product of ComplexWishart Matrices 252 6.11 Spectral Relations between Products and Powers 254 6.12 Products of Finite-Size I.I.D. Gaussian Random Matrices 258 6.13 Lyapunov Exponents for Products of Complex Gaussian Random Matrices 260 6.14 Euclidean Random Matrices 264 6.15 Random Matrices with Independent Entries and the Circular Law 273 6.16 The Circular Law and Outliers 275 6.17 Random SVD, Single Ring Law, and Outliers 285 6.18 The Elliptic Law and Outliers 295 Bibliographical Remarks 305 7 The Mathematical Foundations of Data Collection 307 7.1 Architectures and Applications for Big Data 307 7.2 Covariance Matrix Estimation 308 7.3 Spectral Estimators for Large Random Matrices 312 7.4 Asymptotic Framework for Matrix Reconstruction 319 7.5 Optimum Shrinkage 329 7.6 A Shrinkage Approach to Large-Scale Covariance Matrix Estimation 331 7.7 Eigenvectors of Large Sample Covariance Matrix Ensembles 338 7.8 A General Class of Random Matrices 351 Bibliographical Remarks 359 8 Matrix Hypothesis Testing using Large RandomMatrices 361 8.1 Motivating Examples 362 8.2 Hypothesis Test of Two Alternative Random Matrices 363 8.3 Eigenvalue Bounds for Expectation and Variance 364 8.4 Concentration of Empirical Distribution Functions 369 8.5 Random Quadratic Forms 381 8.6 Log-Determinant of Random Matrices 382 8.7 General MANOVA Matrices 383 8.8 Finite Rank Perturbations of Large Random Matrices 386 8.9 Hypothesis Tests for High-Dimensional Datasets 391 8.9.1 Motivation for Likelihood Ratio Test (LRT) and Covariance Matrix Tests 392 8.10 Roy’s Largest Root Test 428 8.11 Optimal Tests of Hypotheses for Large Random Matrices 431 8.12 Matrix Elliptically Contoured Distributions 444 8.13 Hypothesis Testing for Matrix Elliptically Contoured Distributions 446 Bibliographical Remarks 452 Part II Smart Grid 455 9 Applications and Requirements of Smart Grid 457 9.1 History 457 9.2 Concepts and Vision 458 9.3 Today’s Electric Grid 459 9.4 Future Smart Electrical Energy System 464 10 Technical Challenges for Smart Grid 471 Bibliographical Remarks 483 11 Big Data for Smart Grid 485 11.1 Power in Numbers: Big Data and Grid Infrastructure 485 11.2 Energy’s Internet:The Convergence of Big Data and the Cloud 486 11.3 Edge Analytics: Consumers, Electric Vehicles, and Distributed Generation 486 11.4 CrosscuttingThemes: Big Data 486 11.5 Cloud Computing for Smart Grid 488 11.6 Data Storage, Data Access and Data Analysis 488 11.7 The State-of-the-Art Processing Techniques of Big Data 488 11.8 Big Data Meets the Smart Electrical Grid 488 11.9 4Vs of Big Data: Volume, Variety, Value and Velocity 489 11.10 Cloud Computing for Big Data 490 11.11 Big Data for Smart Grid 490 11.12 Information Platforms for Smart Grid 491 Bibliographical Remarks 491 12 Grid Monitoring and State Estimation 493 12.1 Phase Measurement Unit 493 12.2 Optimal PMU Placement 495 12.3 State Estimation 495 12.4 Basics of State Estimation 495 12.5 Evolution of State Estimation 496 12.6 Static State Estimation 497 12.7 Forecasting-Aided State Estimation 500 12.8 Phasor Measurement Units 501 12.9 Distributed System State Estimation 502 12.10 Event-Triggered Approaches to State Estimation 502 12.11 Bad Data Detection 502 12.12 Improved Bad Data Detection 504 12.13 Cyber-Attacks 504 12.14 Line Outage Detection 504 Bibliographical Remarks 504 13 False Data Injection Attacks against State Estimation 505 13.1 State Estimation 505 13.2 False Data Injection Attacks 507 13.3 MMSE State Estimation and Generalized Likelihood Ratio Test 508 13.4 Sparse Recovery from Nonlinear Measurements 512 13.5 Real-Time Intrusion Detection 515 Bibliographical Remarks 515 14 Demand Response 517 14.1 Why Engage Demand? 517 14.2 Optimal Real-time Pricing Algorithms 520 14.3 Transportation Electrification and Vehicle-to-Grid Applications 522 14.4 Grid Storage 522 Bibliographical Remarks 523 Part III Communications and Sensing 525 15 Big Data for Communications 527 15.1 5G and Big Data 527 15.2 5GWireless Communication Networks 527 15.3 Massive Multiple Input, Multiple Output 528 15.4 Free Probability for the Capacity of the Massive MIMO Channel 537 15.5 Spectral Sensing for Cognitive Radio 539 Bibliographical Remarks 539 16 Big Data for Sensing 541 16.1 Distributed Detection and Estimation 541 16.2 Euclidean Random Matrix 547 16.3 Decentralized Computing 548 Appendix A: Some Basic Results on Free Probability 551 Appendix B: Matrix-Valued Random Variables 557 References 567 Index 601

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Book SynopsisExploit the power and potential of Big Data to revolutionize business outcomes Big Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data.Table of ContentsPrologue 1 Berkeley, 1930s 1 Pattern Recognition 2 Nelson Peltz 3 Committing to One Percent 5 The Big Data Revolution 6 Introduction 7 Storytelling 7 Objective 7 Outline 8 Part I “The Revolution Starts Now: 9 Industries Transforming with Data” 8 Part II “Learning from Patterns in Big Data” 11 Part III “Leading the Revolution” 11 Storytelling (Continued) 13 Part I: the Revolution Starts Now: 9 Industries Transforming With Data 15 Chapter 1: Transforming Farms with Data 17 California, 2013 17 Brief History of Farming 18 The Data Era 19 Potato Farming 20 Precision Farming 21 Capturing Farm Data 22 Deere & Company Versus Monsanto 24 Integrated Farming Systems 25 Data Prevails 26 The Climate Corporation 26 Growsafe Systems 27 Farm of the Future 27 California, 2013 (Continued) 29 Chapter 2: Why Doctors Will Have Math Degrees 31 United States, 2014 31 The History of Medical Education 32 Scientific Method 32 Rise of Specialists 33 We Have a Problem 34 Ben Goldacre 35 Vinod Khosla 35 The Data Era 36 Collecting Data 36 Telemedicine 38 Innovating with Data 40 Implications of a Data-Driven Medical World 42 The Future of Medical School 42 A Typical Medical School 42 A Medical School for the Data Era 43 United States, 2030 44 Chapter 3: Revolutionizing Insurance: Why Actuaries Will Become Data Scientists 45 Middle of Somewhere, 2012 45 Short History of Property & Casualty Insurance and Underwriting 46 Actuarial Science In Insurance 47 Pensions, Insurance, Leases 49 Compound Interest 50 Probability 50 Mortality Data 50 Modern-Day Insurance 51 Eight Weeks to Eight Days 51 Online Policies 52 The Data Era 52 Dynamic Risk Management 52 Catastrophe Risk 54 Open Access Modeling 55 Opportunities 56 Middle of Somewhere, 2012 (Continued) 58 Chapter 4: Personalizing Retail and Fashion 59 Karolina 59 A Brief History of Retail 60 Retail Eras 60 Aristide Boucicaut 61 The Shift 62 The Data Era 63 Stitch Fix 63 Keaton Row 65 Zara 66 Karolina (Continued) 67 Chapter 5: Transforming Customer Relationships with Data 69 Buying a House 69 Brief History of Customer Service 70 Customer Service Over Time 70 Boeing 72 Financial Services 74 The Data Era 75 An Automobile Manufacturer 76 Zendesk 76 Buying a House (Continued) 77 Chapter 6: Intelligent Machines 79 Denmark 79 Intelligent Machines 80 Machine Data 81 The Data Era 82 General Electric 82 Drones 84 Tesla 86 Networks of Data 87 Denmark (Continued) 88 Chapter 7: Government and Society 89 Egypt, 2011 89 Social Media 90 Intelligence 90 Snowden Effect 91 Privacy Risk Versus Reward 91 Observation or Surveillance 93 Development Targets 93 Open Data 95 Hackathons 95 Open Access 95 Ensuring Personal Protection 96 Private Clouds 97 Sanitizing Data 97 Evidence-Based Policy 97 Public-Private Partnerships 98 Impact Bonds 101 Social Impact Bond 102 Development Impact Bonds 103 The Role of Big Data 104 Egypt, 2011 (Continued) 105 Chapter 8: Corporate Sustainability 107 City of London 107 Global Megaforces 109 Population 109 Carbon Footprint 110 Water Scarcity 110 Environmental Risk 111 BP and Exxon Mobile 111 Early Warning Systems 112 Social Media 113 Risk and Resilience 114 Measuring Sustainability 115 Long-Term Decision Making 116 Stranded Assets 117 City of London (Continued) 118 Chapter 9: Weather and Energy 119 India, 2012 119 The Weather 120 Forecasting the Weather 120 When are Weather Forecasts Wrong? 121 Chaos 122 Ensemble Forecasts 122 Communication 123 Renewable Energy 124 Solar, Hydro, and Wind Power 124 Volatile or Intermittent Supply 125 Energy Consumption 126 Smart Meters 127 Intelligent Demand-Side Management 128 India, 2012 (Continued) 129 Part II: Learning From Patterns in Big Data 131 Chapter 10: Pattern Recognition 133 Elements of Success Rhyme 133 Pattern Recognition: A Gift or Trap? 134 What Fish Teach Us About Pattern Recognition 135 Bayes’ Theorem 135 Tsukiji Market 135 Pattern Recognition 137 Rochester Institute of Technology 137 A Method for Recognizing Patterns 137 Elements of Success Rhyme (Continued) 140 Chapter 11: Why Patterns in Big Data Have Emerged 141 Meatpacking District 141 Business Models in the Data Era 142 Data as a Competitive Advantage 143 Data Improves Existing Products or Services 145 Data as the Product 145 Dun & Bradstreet 146 CoStar 148 Ihs 149 Meatpacking District (Continued) 151 Chapter 12: Patterns in Big Data 153 The Data Factor 154 Summary of Big Data Patterns 155 Redefining a Skilled Worker 155 Creating and Utilizing New Sources of Data 156 Building New Data Applications 157 Transforming and Creating New Business Processes 157 Data Collection for Competitive Advantage 158 Exposing Opinion-Based Biases 159 Real-Time Monitoring and Decision Making 159 Social Networks Leveraging and Creating Data 160 Deconstructing the Value Chain 161 New Product Offerings 161 Building for Customers Instead of Markets 162 Tradeoff Between Privacy and Insight 163 Changing the Definition of a Product 163 Inverting the Search Paradigm for Data Discovery 164 Data Security 165 New Partnerships Founded on Data 165 Shortening the Innovation Lifecycle 166 Defining New Channels to Market 166 New Economic Models 167 Forecasting and Predicting Future Events 168 Changing Incentives 168 New Partnerships (Public/Private) 169 Real-Time Monitoring and Decision Making (Early Warning Systems) 169 A Framework for Big Data Patterns 170 Part III: Leading the Revolution 171 Chapter 13: The Data Opportunity 173 What Oil Teaches Us About Data 173 Bain Study 175 Seizing the Opportunity 176 Chapter 14: Porsche 177 Rome 177 Ferdinand Porsche 178 The Birth of Porsche 178 The Porsche Sports Car 179 Porsche Today 180 Rome (Continued) 180 Chapter 15: Puma 181 Herzogenaurach 181 Advertising Wars 182 Jochen Zeitz 182 Environmental Profit and Loss 183 Herzogenaurach (Continued) 184 Chapter 16: A Methodology for Applying Big Data Patterns 185 Introduction 185 The Method 186 Step 1: Understand Data Assets 187 The Patterns 188 Step 2: Explore Data 191 Challenges 192 Questions 192 Hypotheses 193 Data 193 Models 193 Statistical Significance 194 Step 3: Design the Future 194 The Patterns 195 Step 4: Design a Data-Driven Business Model 197 The Patterns 197 Step 5: Transform Business Processes for the Data Era 199 The Patterns 199 Step 6: Design for Governance and Security 201 The Patterns 201 Step 7: Share Metrics and Incentives 202 Chapter 17: Big Data Architecture 205 Introduction 205 Architect for the Future 206 Lessons from Stuttgart 207 Big Data Reference Architectures 207 Leveraging Investments in Architecture 208 Big Data Reference Architectures 211 Business View 212 Logical View 213 Chapter 18: Business View Reference Architecture 215 Introduction 215 Men’s Trunk: A Retailer in the Data Era 216 The Business View Reference Architecture 217 Answer Fabric 218 Data Virtualization 219 Data Engines 220 Management 221 Data Governance 221 User Interface, Applications, and Business Processes 222 Summary 222 Chapter 19: Logical View Reference Architecture 223 Introduction 223 Men’s Trunk: A Retailer in the Data Era (Continued) 224 The Logical View Reference Architecture 226 Data Ingest 227 Analytics 227 Discovery 228 Landing 228 Operational Warehouse 229 Information Insight 230 Operational Data 231 Governance 231 Men’s Trunk: A Retailer in the Data Era (Continued) 232 Chapter 20: The Architecture of the Future 233 Men’s Trunk: A Retailer in the Data Era (Continued) 233 Men’s Trunk: Applying the Methodology 235 Step 1: Understand Data Assets 235 Step 2: Explore the Data 236 Step 3: Design the Future 237 Step 4: Design a Data-Driven Business Model 237 Step 5: Transform Business Processes for the Data Era 237 Step 6: Design for Governance and Security 237 Step 7: Share Metrics and Incentives 238 Men’s Trunk: The Business View Reference Architecture 239 Answer Fabric 240 Data Virtualization 241 Data Engines 241 Management 242 Data Governance 242 User Interface, Applications, and Business Processes 243 Men’s Trunk: The Logical View Reference Architecture 244 Approach 244 Men’s Trunk: A Retailer in the Data Era (Continued) 248 Epilogue 249 The Time is Now 249 Taking Action 250 Fear not Usual Competitors 251 The Future 252 Index 255

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Book SynopsisAn essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data.Table of ContentsForeword xi Acknowledgments xv Introduction 1 Who Should Read This Book? 3 What’s in This Book? 4 How to Contact Us 6 Chapter 1 Healthcare, History, and Heartbreak 7 Top Issues in Healthcare 9 Data Management 16 Biosimilars, Drug Pricing, and Pharmaceutical Compounding 18 Promising Areas of Innovation 19 Conclusion 25 Notes 25 Chapter 2 Genome Sequencing: Know Thyself, One Base Pair at a Time 27 Content contributed by Sheetal Shetty and Jacob Brill Challenges of Genomic Analysis 29 The Language of Life 30 A Brief History of DNA Sequencing 31 DNA Sequencing and the Human Genome Project 35 Select Tools for Genomic Analysis 38 Conclusion 47 Notes 48 Chapter 3 Data Management 53 Content contributed by Joe Arnold Bits about Data 54 Data Types 56 Data Security and Compliance 59 Data Storage 66 SwiftStack 70 OpenStack Swift Architecture 78 Conclusion 94 Notes 94 Chapter 4 Designing a Data-Ready Network Infrastructure 105 Research Networks: A Primer 108 ESnet at 30: Evolving toward Exascale and Raising Expectations 109 Internet2 Innovation Platform 111 Advances in Networking 113 InfiniBand and Microsecond Latency 114 The Future of High-Performance Fabrics 117 Network Function Virtualization 119 Software-Defined Networking 121 OpenDaylight 122 Conclusion 157 Notes 157 Chapter 5 Data-Intensive Compute Infrastructures 163 Content contributed by Dijiang Huang, Yuli Deng, Jay Etchings, Zhiyuan Ma, and Guangchun Luo Big Data Applications in Health Informatics 166 Sources of Big Data in Health Informatics 168 Infrastructure for Big Data Analytics 171 Fundamental System Properties 186 GPU-Accelerated Computing and Biomedical Informatics 187 Conclusion 190 Notes 191 Chapter 6 Cloud Computing and Emerging Architectures 211 Cloud Basics 213 Challenges Facing Cloud Computing Applications in Biomedicine 215 Hybrid Campus Clouds 216 Research as a Service 217 Federated Access Web Portals 219 Cluster Homogeneity 220 Emerging Architectures (Zeta Architecture) 221 Conclusion 229 Notes 229 Chapter 7 Data Science 235 NoSQL Approaches to Biomedical Data Science 237 Using Splunk for Data Analytics 244 Statistical Analysis of Genomic Data with Hadoop 250 Extracting and Transforming Genomic Data 253 Processing eQTL Data 256 Generating Master SNP Files for Cases and Controls 259 Generating Gene Expression Files for Cases and Controls 260 Cleaning Raw Data Using MapReduce 261 Transpose Data Using Python 263 Statistical Analysis Using Spark 264 Hive Tables with Partitions 268 Conclusion 270 Notes 270 Appendix: A Brief Statistics Primer 290 Content Contributed by Daniel Peñaherrera Chapter 8 Next-Generation Cyberinfrastructures 307 Next-Generation Cyber Capability 308 NGCC Design and Infrastructure 310 Conclusion 327 Note 330 Conclusion 335 Appendix A The Research Data Management Survey: From Concepts to Practice 337 Brandon Mikkelsen and Jay Etchings Appendix B Central IT and Research Support 353 Gregory D. Palmer Appendix C HPC Working Example: Using Parallelization Programs Such as GNU Parallel and OpenMP with Serial Tools 377 Appendix D HPC and Hadoop: Bridging HPC to Hadoop 385 Appendix E Bioinformatics + Docker: Simplifying Bioinformatics Tools Delivery with Docker Containers 391 Glossary 399 About the Author 419 About the Contributors 421 Index 427

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Book SynopsisGet to know the why' and how' of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it's a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. Gain a solid reason to use machine learning Frame your question using financial markets laws Know your data Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effectivTable of ContentsCHAPTER 1 Do Algorithms Dream About Artificial Alphas? 1By Michael Kollo CHAPTER 2 Taming Big Data 13By Rado Lipuš and Daryl Smith CHAPTER 3 State of Machine Learning Applications in Investment Management 33By Ekaterina Sirotyuk CHAPTER 4 Implementing Alternative Data in an Investment Process 51By Vinesh Jha CHAPTER 5 Using Alternative and Big Data to Trade Macro Assets 75By Saeed Amen and Iain Clark CHAPTER 6 Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales 95By Giuliano De Rossi, Jakub Kolodziej and Gurvinder Brar CHAPTER 7 Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework 129By Tony Guida and Guillaume Coqueret CHAPTER 8 A Social Media Analysis of Corporate Culture 149By Andy Moniz CHAPTER 9 Machine Learning and Event Detection for Trading Energy Futures 169By Peter Hafez and Francesco Lautizi CHAPTER 10 Natural Language Processing of Financial News 185By M. Berkan Sesen, Yazann Romahi and Victor Li CHAPTER 11 Support Vector Machine-Based Global Tactical Asset Allocation 211By Joel Guglietta CHAPTER 12 Reinforcement Learning in Finance 225By Gordon Ritter CHAPTER 13 Deep Learning in Finance: Prediction of Stock Returns with Long Short-Term Memory Networks 251By Miquel N. Alonso, Gilberto Batres-Estrada and Aymeric Moulin Biography 279

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Book SynopsisIntroduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuseTable of ContentsForeword xxv About the Author xxvii Acknowledgements xxix Preface xxxi About the Companion Site xxxv I Introduction 1 1 The Big Picture with Kondratiev and Kardashev 3 2 The Scientific Method and Data 7 3 Conventions 11 II Starting with R and Elements of Statistics 19 4 The Basics of R 21 4.1 Getting Started with R 23 4.2 Variables 26 4.3 Data Types 28 4.3.1 The Elementary Types 28 4.3.2 Vectors 29 4.3.3 Accessing Data from a Vector 29 4.3.4 Matrices 32 4.3.5 Arrays 38 4.3.6 Lists 41 4.3.7 Factors 45 4.3.8 Data Frames 49 4.3.9 Strings or the Character-type 54 4.4 Operators 57 4.4.1 Arithmetic Operators 57 4.4.2 Relational Operators 57 4.4.3 Logical Operators 58 4.4.4 Assignment Operators 59 4.4.5 Other Operators 61 4.5 Flow Control Statements 63 4.5.1 Choices 63 4.5.2 Loops 65 4.6 Functions 69 4.6.1 Built-in Functions 69 4.6.2 Help with Functions 69 4.6.3 User-defined Functions 70 4.6.4 Changing Functions 70 4.6.5 Creating Function with Default Arguments 71 4.7 Packages 72 4.7.1 Discovering Packages in R 72 4.7.2 Managing Packages in R 73 4.8 Selected Data Interfaces 75 4.8.1 CSV Files 75 4.8.2 Excel Files 79 4.8.3 Databases 79 5 Lexical Scoping and Environments 81 5.1 Environments in R 81 5.2 Lexical Scoping in R 83 6 The Implementation of OO 87 6.1 Base Types 89 6.2 S3 Objects 91 6.2.1 Creating S3 Objects 94 6.2.2 Creating Generic Methods 96 6.2.3 Method Dispatch 97 6.2.4 Group Generic Functions 98 6.3 S4 Objects 100 6.3.1 Creating S4 Objects 100 6.3.2 Using S4 Objects 101 6.3.3 Validation of Input 105 6.3.4 Constructor functions 107 6.3.5 The Data slot 108 6.3.6 Recognising Objects, Generic Functions, and Methods 108 6.3.7 CreatingS4Generics 110 6.3.8 Method Dispatch 111 6.4 The Reference Class, refclass, RC or R5 Model 113 6.4.1 Creating RC Objects 113 6.4.2 Important Methods and Attributes 117 6.5 Conclusions about the OO Implementation 119 7 Tidy R with the Tidyverse 121 7.1 The Philosophy of the Tidyverse 121 7.2 Packages in the Tidyverse 124 7.2.1 The Core Tidyverse 124 7.2.2 The Non-core Tidyverse 125 7.3 Working with the Tidyverse 127 7.3.1 Tibbles 127 7.3.2 Piping with R 132 7.3.3 Attention Points When Using the Pipe 133 7.3.4 Advanced Piping 134 7.3.5 Conclusion 137 8 Elements of Descriptive Statistics 139 8.1 Measures of Central Tendency 139 8.1.1 Mean 139 8.1.2 The Median 142 8.1.3 The Mode 143 8.2 Measures of Variation or Spread 145 8.3 Measures of Covariation 147 8.3.1 The Pearson Correlation 147 8.3.2 The Spearman Correlation 148 8.3.3 Chi-square Tests 149 8.4 Distributions 150 8.4.1 Normal Distribution 150 8.4.2 Binomial Distribution 153 8.5 Creating an Overview of Data Characteristics 155 9 Visualisation Methods 159 9.1 Scatterplots 161 9.2 Line Graphs 163 9.3 Pie Charts 165 9.4 Bar Charts 167 9.5 Boxplots 171 9.6 Violin Plots 173 9.7 Histograms 176 9.8 Plotting Functions 179 9.9 Maps and Contour Plots 180 9.10 Heat-maps 181 9.11 Text Mining 184 9.11.1 Word Clouds 184 9.11.2 Word Associations 188 9.12 Colours in R 191 10 Time Series Analysis 197 10.1 Time Series in R 197 10.1.1 The Basics of Time Series in R 197 10.2 Forecasting 200 10.2.1 Moving Average 200 10.2.2 Seasonal Decomposition 206 11 Further Reading 211 III Data Import 213 12 A Short History of Modern Database Systems 215 13 RDBMS 219 14 SQL 223 14.1 Designing the Database 223 14.2 Building the Database Structure 226 14.2.1 Installing a RDBMS 226 14.2.2 Creating the Database 228 14.2.3 Creating the Tables and Relations 229 14.3 Adding Data to the Database 235 14.4 Querying the Database 239 14.4.1 The Basic Select Query 239 14.4.2 More Complex Queries 240 14.5 Modifying the Database Structure 244 14.6 Selected Features of SQL 249 14.6.1 Changing Data 249 14.6.2 Functions in SQL 249 15 Connecting R to an SQL Database 253 IV Data Wrangling 257 16 Anonymous Data 261 17 Data Wrangling in the tidyverse 265 17.1 Importing the Data 266 17.1.1 Importing from an SQLRDBMS 266 17.1.2 Importing Flat Files in the Tidyverse 267 17.2 Tidy Data 275 17.3 Tidying Up Data with tidyr 277 17.3.1 Splitting Tables 278 17.3.2 Convert Headers to Data 281 17.3.3 Spreading One Column Over Many 284 17.3.4 Split One Columns into Many 285 17.3.5 Merge Multiple Columns Into One 286 17.3.6 Wrong Data 287 17.4 SQL-like Functionality via dplyr 288 17.4.1 Selecting Columns 288 17.4.2 Filtering Rows 289 17.4.3 Joining 290 17.4.4 Mutating Data 293 17.4.5 Set Operations 296 17.5 String Manipulation in the tidyverse 299 17.5.1 Basic String Manipulation 300 17.5.2 Pattern Matching with Regular Expressions 302 17.6 Dates with lubridate 314 17.6.1 ISO 8601 Format 315 17.6.2 Time-zones 317 17.6.3 Extract Date and Time Components 318 17.6.4 Calculating with Date-times 319 17.7 Factors with Forcats 325 18 Dealing with Missing Data 333 18.1 Reasons for Data to be Missing 334 18.2 Methods to Handle Missing Data 336 18.2.1 Alternative Solutions to Missing Data 336 18.2.2 Predictive Mean Matching(PMM) 338 18.3 R Packages to Deal with Missing Data 339 18.3.1 mice 339 18.3.2 missForest 340 18.3.3 Hmisc 341 19 Data Binning 343 19.1 What is Binning and Why Use It 343 19.2 Tuning the Binning Procedure 347 19.3 More Complex Cases: Matrix Binning 352 19.4 Weight of Evidence and Information Value 359 19.4.1 Weight of Evidence(WOE) 359 19.4.2 Information Value(IV) 359 19.4.3 WOE and IV in R 359 20 Factoring Analysis and Principle Components 363 20.1 Principle Components Analysis (PCA) 364 20.2 Factor Analysis 368 V Modelling 373 21 Regression Models 375 21.1 Linear Regression 375 21.2 Multiple Linear Regression 379 21.2.1 Poisson Regression 379 21.2.2 Non-linear Regression 381 21.3 Performance of Regression Models 384 21.3.1 Mean Square Error (MSE) 384 21.3.2 R-Squared 384 21.3.3 Mean Average Deviation(MAD) 386 22 Classification Models 387 22.1 Logistic Regression 388 22.2 Performance of Binary Classification Models 390 22.2.1 The Confusion Matrix and Related Measures 391 22.2.2 ROC 393 22.2.3 The AUC 396 22.2.4 The Gini Coefficient 397 22.2.5 Kolmogorov-Smirnov (KS) for Logistic Regression 398 22.2.6 Finding an Optimal Cut-off 399 23 Learning Machines 405 23.1 Decision Tree 407 23.1.1 Essential Background 407 23.1.2 Important Considerations 412 23.1.3 Growing Trees with the Package rpart 414 23.1.4 Evaluating the Performance of a Decision Tree 424 23.2 Random Forest 428 23.3 Artificial Neural Networks (ANNs) 434 23.3.1 The Basics of ANNs in R 434 23.3.2 Neural Networks in R 436 23.3.3 The Work-flow to for Fitting a NN 438 23.3.4 Cross Validate the NN 444 23.4 Support Vector Machine 447 23.4.1 Fitting a SVM in R 447 23.4.2 Optimizing the SVM 449 23.5 Unsupervised Learning and Clustering 450 23.5.1 k-Means Clustering 450 23.5.2 Visualizing Clusters in Three Dimensions 462 23.5.3 Fuzzy Clustering 464 23.5.4 Hierarchical Clustering 466 23.5.5 Other Clustering Methods 468 24 Towards a Tidy Modelling Cycle with modelr 469 24.1 Adding Predictions 470 24.2 Adding Residuals 471 24.3 Bootstrapping Data 472 24.4 Other Functions of modelr 474 25 Model Validation 475 25.1 Model Quality Measures 476 25.2 Predictions and Residuals 477 25.3 Bootstrapping 479 25.3.1 Bootstrapping in Base R 479 25.3.2 Bootstrapping in the tidyverse with modelr 481 25.4 Cross-Validation 483 25.4.1 Elementary Cross Validation 483 25.4.2 Monte Carlo Cross Validation 486 25.4.3 k-Fold Cross Validation 488 25.4.4 Comparing Cross Validation Methods 489 25.5 Validation in a Broader Perspective 492 26 Labs 495 26.1 Financial Analysis with quantmod 495 26.1.1 The Basics of quantmod 495 26.1.2 Types of Data Available in quantmod 496 26.1.3 Plotting with quantmod 497 26.1.4 The quantmod Data Structure 500 26.1.5 Support Functions Supplied by quantmod 502 26.1.6 Financial Modelling in quantmod 504 27 Multi Criteria Decision Analysis (MCDA) 511 27.1 What and Why 511 27.2 General Work-flow 513 27.3 Identify the Issue at Hand: Steps 1 and 2 516 27.4 Step3: the Decision Matrix 518 27.4.1 Construct a Decision Matrix 518 27.4.2 Normalize the Decision Matrix 520 27.5 Step 4: Delete Inefficient and Unacceptable Alternatives 521 27.5.1 Unacceptable Alternatives 521 27.5.2 Dominance – Inefficient Alternatives 521 27.6 Plotting Preference Relationships 524 27.7 Step5: MCDA Methods 526 27.7.1 Examples of Non-compensatory Methods 526 27.7.2 The Weighted Sum Method(WSM) 527 27.7.3 Weighted Product Method(WPM) 530 27.7.4 ELECTRE 530 27.7.5 PROMethEE 540 27.7.6 PCA(Gaia) 553 27.7.7 Outranking Methods 557 27.7.8 Goal Programming 558 27.8 Summary MCDA 561 VI Introduction to Companies 563 28 Financial Accounting (FA) 567 28.1 The Statements of Accounts 568 28.1.1 Income Statement 568 28.1.2 Net Income: The P&L statement 568 28.1.3 Balance Sheet 569 28.2 The Value Chain 571 28.3 Further, Terminology 573 28.4 Selected Financial Ratios 575 29 Management Accounting 583 29.1 Introduction 583 29.1.1 Definition of Management Accounting (MA) 583 29.1.2 Management Information Systems (MIS) 584 29.2 Selected Methods in MA 585 29.2.1 Cost Accounting 585 29.2.2 Selected Cost Types 587 29.3 Selected Use Cases of MA 590 29.3.1 Balanced Scorecard 590 29.3.2 Key Performance Indicators (KPIs) 591 30 Asset Valuation Basics 597 30.1 Time Value of Money 598 30.1.1 Interest Basics 598 30.1.2 Specific Interest Rate Concepts 598 30.1.3 Discounting 600 30.2 Cash 601 30.3 Bonds 602 30.3.1 Features of a Bond 602 30.3.2 Valuation of Bonds 604 30.3.3 Duration 606 30.4 The Capital Asset Pricing Model (CAPM) 610 30.4.1 The CAPM Framework 610 30.4.2 The CAPM and Risk 612 30.4.3 Limitations and Shortcomings of the CAPM 612 30.5 Equities 614 30.5.1 Definition 614 30.5.2 Short History 614 30.5.3 Valuation of Equities 615 30.5.4 Absolute Value Models 616 30.5.5 Relative Value Models 625 30.5.6 Selection of Valuation Methods 630 30.5.7 Pitfalls in Company Valuation 631 30.6 Forwards and Futures 638 30.7 Options 640 30.7.1 Definitions 640 30.7.2 Commercial Aspects 642 30.7.3 Short History 643 30.7.4 Valuation of Options at Maturity 644 30.7.5 The Black and Scholes Model 649 30.7.6 The Binomial Model 654 30.7.7 Dependencies of the Option Price 660 30.7.8 The Greeks 664 30.7.9 Delta Hedging 665 30.7.10 Linear Option Strategies 667 30.7.11 Integrated Option Strategies 674 30.7.12 Exotic Options 678 30.7.13 Capital Protected Structures 680 VII Reporting 683 31 A Grammar of Graphics with ggplot2 687 31.1 TheBasicsofggplot2 688 31.2 Over-plotting 692 31.3 CaseStudyforggplot2 696 32 R Markdown 699 33 knitr and LATEX 703 34 An Automated Development Cycle 707 35 Writing and Communication Skills 709 36 Interactive Apps 713 36.1 Shiny 715 36.2 Browser Born Data Visualization 719 36.2.1 HTML-widgets 719 36.2.2 Interactive Maps with leaflet 720 36.2.3 Interactive Data Visualisation with ggvis 721 36.2.4 googleVis 723 36.3 Dashboards 725 36.3.1 The Business Case: a Diversity Dashboard 726 36.3.2 A Dashboard with flexdashboard 731 36.3.3 A Dashboard with shinydashboard 737 VIII Bigger and Faster R 741 37 Parallel Computing 743 37.1 Combine foreach and doParallel 745 37.2 Distribute Calculations over LAN with Snow 748 37.3 Using the GPU 752 37.3.1 Getting Started with gpuR 754 37.3.2 On the Importance of Memory use 757 37.3.3 Conclusions for GPU Programming 759 38 R and Big Data 761 38.1 Use a Powerful Server 763 38.1.1 Use R on a Server 763 38.1.2 Let the Database Server do the Heavy Lifting 763 38.2 Using more Memory than we have RAM 765 39 Parallelism for Big Data 767 39.1 Apache Hadoop 769 39.2 Apache Spark 771 39.2.1 Installing Spark 771 39.2.2 Running Spark 773 39.2.3 SparkR 776 39.2.4 sparklyr 788 39.2.5 SparkR or sparklyr 791 40 The Need for Speed 793 40.1 Benchmarking 794 40.2 Optimize Code 797 40.2.1 Avoid Repeating the Same 797 40.2.2 Use Vectorisation where Appropriate 797 40.2.3 Pre-allocating Memory 799 40.2.4 Use the Fastest Function 800 40.2.5 Use the Fastest Package 801 40.2.6 Be Mindful about Details 802 40.2.7 Compile Functions 804 40.2.8 Use C or C++ Code in R 806 40.2.9 Using a C++ Source File in R 809 40.2.10CallCompiledC++Functions in R 811 40.3 Profiling Code 812 40.3.1 The Package profr 813 40.3.2 The Package proftools 813 40.4 Optimize Your Computer 817 IX Appendices 819 A Create your own R Package 821 A.1 Creating the Package in the R Console 823 A.2 Update the Package Description 825 A.3 Documenting the Functionsxs 826 A.4 Loading the Package 827 A.5 Further Steps 828 B Levels of Measurement 829 B.1 Nominal Scale 829 B.2 Ordinal Scale 830 B.3 Interval Scale 831 B.4 Ratio Scale 832 C Trademark Notices 833 C.1 General Trademark Notices 834 C.2 R-Related Notices 835 C.2.1 Crediting Developers of R Packages 835 C.2.2 The R-packages used in this Book 835 D Code Not Shown in the Body of the Book 839 E Answers to Selected Questions 845 Bibliography 859 Nomenclature 869 Index 881

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Book SynopsisCOGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention. As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. This book tackles all these issues and provides insight into these diversifieTable of ContentsPreface xv 1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta 1.1 Introduction 2 1.2 Cognitive Science 3 1.3 Gap Between Classical Theory of Cognition 4 1.4 Cognitive Computing’s Evolution 6 1.5 The Coming Era of Cognitive Computing 7 1.6 Cognitive Computing Architecture 9 1.6.1 The Internet-of-Things and Cognitive Computing 10 1.6.2 Big Data and Cognitive Computing 11 1.6.3 Cognitive Computing and Cloud Computing 13 1.7 Enabling Technologies in Cognitive Computing 13 1.7.1 Reinforcement Learning and Cognitive Computing 13 1.7.2 Cognitive Computing with Deep Learning 15 1.7.2.1 Relational Technique and Perceptual Technique 15 1.7.2.2 Cognitive Computing and Image Understanding 16 1.8 Intelligent Systems in Healthcare 17 1.8.1 Intelligent Cognitive System in Healthcare (Why and How) 20 1.9 The Cognitive Challenge 32 1.9.1 Case Study: Patient Evacuation 32 1.9.2 Case Study: Anesthesiology 32 1.10 Conclusion 34 References 35 2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano 2.1 Introduction 42 2.2 Literature Concept 44 2.2.1 Cognitive Computing Concept 44 2.2.2 Neural Networks Concepts 47 2.2.3 Convolutional Neural Network 49 2.2.4 Deep Learning 52 2.3 Materials and Methods (Metaheuristic Algorithm Proposal) 55 2.4 Case Study and Discussion 57 2.5 Conclusions with Future Research Scopes 60 References 61 3 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran 3.1 Introduction 68 3.2 Literature Review 70 3.2.1 Role of Cognitive Computing in Healthcare Applications 70 3.2.2 Research Problem Study by IBM 73 3.2.3 Purpose of Big Data in Healthcare 74 3.2.4 Convergence of Big Data with Cognitive Computing 74 3.2.4.1 Smart Healthcare 74 3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 75 3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 76 3.3.1 EEG Pathology Diagnoses 76 3.3.2 Cognitive–Big Data-Based Smart Healthcare 77 3.3.3 System Architecture 79 3.3.4 Detection and Classification of Pathology 80 3.3.4.1 EEG Preprocessing and Illustration 80 3.3.4.2 CNN Model 80 3.3.5 Case Study 81 3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 83 3.4.1 Cloud Computing with Big Data in Healthcare 86 3.4.2 Heart Diseases 87 3.4.3 Healthcare Big Data Techniques 88 3.4.3.1 Rule Set Classifiers 88 3.4.3.2 Neuro Fuzzy Classifiers 89 3.4.3.3 Experimental Results 91 3.5 Conclusion 92 References 93 4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh 4.1 Introduction 98 4.2 The Role of Technology in an Aging Society 99 4.3 Literature Survey 100 4.4 Health Monitoring 101 4.5 Nutrition Monitoring 105 4.6 Stress-Log: An IoT-Based Smart Monitoring System 106 4.7 Active Aging 108 4.8 Localization 108 4.9 Navigation Care 111 4.10 Fall Monitoring 113 4.10.1 Fall Detection System Architecture 114 4.10.2 Wearable Device 114 4.10.3 Wireless Communication Network 114 4.10.4 Smart IoT Gateway 115 4.10.5 Interoperability 115 4.10.6 Transformation of Data 115 4.10.7 Analyzer for Big Data 115 4.11 Conclusion 115 References 116 5 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar 5.1 Introduction 122 5.2 Bond Between Big Data and Cognitive Computing 124 5.3 Need for Cognitive Computing in Healthcare 126 5.4 Conceptual Model Linking Big Data and Cognitive Computing 128 5.4.1 Significance of Big Data 128 5.4.2 The Need for Cognitive Computing 129 5.4.3 The Association Between the Big Data and Cognitive Computing 130 5.4.4 The Advent of Cognition in Healthcare 132 5.5 IBM’s Watson and Cognitive Computing 133 5.5.1 Industrial Revolution with Watson 134 5.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 135 5.6 Future Directions 137 5.6.1 Retail 138 5.6.2 Research 139 5.6.3 Travel 139 5.6.4 Security and Threat Detection 139 5.6.5 Cognitive Training Tools 140 5.7 Conclusion 141 References 141 6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano 6.1 Introduction 146 6.2 Literature Concept 148 6.2.1 Cognitive Computing Concept 148 6.2.1.1 Application Potential 151 6.2.2 Cognitive Computing in Healthcare 153 6.2.3 Deep Learning in Healthcare 157 6.2.4 Natural Language Processing in Healthcare 160 6.3 Discussion 162 6.4 Trends 163 6.5 Conclusions 164 References 165 7 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa 7.1 Introduction 170 7.2 Literature Survey 175 7.3 Two-Factor Authentication 177 7.3.1 Novel Features of Two-Factor Authentication 178 7.3.2 Two-Factor Authentication Sorgen 178 7.3.3 Two-Factor Security Libraries 179 7.3.4 Challenges for Fitness Concern 180 7.4 Proposed Methodology 181 7.5 Medical Treatment and the Preservation of Records 186 7.5.1 Remote Method of Control 187 7.5.2 Enabling Healthcare System Technology 187 7.6 Conclusion 189 References 190 8 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi 8.1 An Overview of Healthcare Systems 198 8.2 Need of Healthcare Systems 198 8.3 Basic Principle of Healthcare Systems 199 8.4 Design and Recommended Structure of Healthcare Systems 199 8.4.1 Healthcare System Designs on the Basis of these Parameters 200 8.4.2 Details of Healthcare Organizational Structure 201 8.5 Various Challenges in Conventional Existing Healthcare System 202 8.6 Health Informatics 202 8.7 Information Technology Use in Healthcare Systems 203 8.8 Details of Various Information Technology Application Use in Healthcare Systems 203 8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 204 8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 205 8.11 Healthcare Data Analytics 206 8.12 Healthcare as a Concept 206 8.13 Healthcare’s Key Technologies 207 8.14 The Present State of Smart Healthcare Application 207 8.15 Data Analytics with Machine Learning Use in Healthcare Systems 208 8.16 Benefit of Data Analytics in Healthcare System 210 8.17 Data Analysis and Visualization: COVID-19 Case Study in India 210 8.18 Bioinformatics Data Analytics 222 8.18.1 Notion of Bioinformatics 222 8.18.2 Bioinformatics Data Challenges 222 8.18.3 Sequence Analysis 222 8.18.4 Applications 223 8.18.5 COVID-19: A Bioinformatics Approach 224 8.19 Conclusion 224 References 225 9 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha 9.1 Introduction 230 9.1.1 Parkinson’s Disease 230 9.1.2 Spect Scan 231 9.2 Literature Survey 232 9.3 Methods and Materials 233 9.3.1 Database Details 233 9.3.2 Procedure 234 9.3.3 Pre-Processing Done by PPMI 235 9.3.4 Image Analysis and Features Extraction 235 9.3.4.1 Image Slicing 235 9.3.4.2 Intensity Normalization 237 9.3.4.3 Image Segmentation 239 9.3.4.4 Shape Features Extraction 240 9.3.4.5 SBR Features 241 9.3.4.6 Feature Set Analysis 242 9.3.4.7 Surface Fitting 242 9.3.5 Classification Modeling 243 9.3.6 Feature Importance Estimation 246 9.3.6.1 Need for Analysis of Important Features 246 9.3.6.2 Random Forest 247 9.4 Results and Discussion 248 9.4.1 Segmentation 248 9.4.2 Shape Analysis 249 9.4.3 Classification 249 9.5 Conclusion 252 References 253 10 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta 10.1 Introduction 258 10.2 Need for Big Data Analytics 260 10.3 Characteristics of Big Data 264 10.3.1 Volume 264 10.3.2 Velocity 265 10.3.3 Variety 265 10.3.4 Veracity 265 10.3.5 Value 265 10.3.6 Validity 265 10.3.7 Variability 266 10.3.8 Viscosity 266 10.3.9 Virality 266 10.3.10 Visualization 266 10.4 Big Data Analysis in Disease Treatment and Management 267 10.4.1 For Diabetes 267 10.4.2 For Heart Disease 268 10.4.3 For Chronic Disease 270 10.4.4 For Neurological Disease 271 10.4.5 For Personalized Medicine 271 10.5 Big Data: Databases and Platforms in Healthcare 279 10.6 Importance of Big Data in Healthcare 285 10.6.1 Evidence-Based Care 285 10.6.2 Reduced Cost of Healthcare 285 10.6.3 Increases the Participation of Patients in the Care Process 285 10.6.4 The Implication in Health Surveillance 285 10.6.5 Reduces Mortality Rate 285 10.6.6 Increase of Communication Between Patients and Healthcare Providers 286 10.6.7 Early Detection of Fraud and Security Threats in Health Management 286 10.6.8 Improvement in the Care Quality 286 10.7 Application of Big Data Analytics 286 10.7.1 Image Processing 286 10.7.2 Signal Processing 287 10.7.3 Genomics 288 10.7.4 Bioinformatics Applications 289 10.7.5 Clinical Informatics Application 291 10.8 Conclusion 293 References 294 11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi 11.1 Introduction 304 11.1.1 Glaucoma 304 11.2 Literature Survey 306 11.3 Methodology 309 11.3.1 Sclera Segmentation 310 11.3.1.1 Fully Convolutional Network 311 11.3.2 Pupil/Iris Ratio 313 11.3.2.1 Canny Edge Detection 314 11.3.2.2 Mean Redness Level (MRL) 315 11.3.2.3 Red Area Percentage (RAP) 316 11.4 Results and Discussion 317 11.4.1 Feature Extraction from Frontal Eye Images 318 11.4.1.1 Level of Mean Redness (MRL) 318 11.4.1.2 Percentage of Red Area (RAP) 318 11.4.2 Images of the Frontal Eye Pupil/Iris Ratio 318 11.4.2.1 Histogram Equalization 319 11.4.2.2 Morphological Reconstruction 319 11.4.2.3 Canny Edge Detection 319 11.4.2.4 Adaptive Thresholding 320 11.4.2.5 Circular Hough Transform 321 11.4.2.6 Classification 322 11.5 Conclusion and Future Work 324 References 325 12 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh 12.1 Introduction 328 12.2 Introduction to Big Data and Data Mining 328 12.3 Role of Sentimental Analysis in the Healthcare Sector 330 12.4 Case Study: Analyzing Mental Health 332 12.4.1 Problem Statement 332 12.4.2 Research Objectives 333 12.4.3 Methodology and Framework 333 12.4.3.1 Big 5 Personality Model 333 12.4.3.2 Openness to Explore 334 12.4.3.3 Methodology 335 12.4.3.4 Detailed Design Methodologies 340 12.4.3.5 Work Done Details as Required 341 12.5 Results and Discussion 343 12.6 Conclusion and Future 345 References 346 13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav 13.1 Introduction 350 13.2 Artificial Intelligence and Management of Chronic Diseases 351 13.3 Blockchain and Healthcare 354 13.3.1 Blockchain and Healthcare Management of Chronic Disease 355 13.4 Internet-of-Things and Healthcare Management of Chronic Disease 358 13.5 Conclusions 360 References 360 14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri 14.1 Introduction 367 14.2 Cognitive Computing Framework in Healthcare 371 14.3 Benefits of Using Cognitive Computing for Healthcare 372 14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 374 14.4.1 Using Cognitive Services for a Patient’s Healthcare Management 375 14.4.2 Using Cognitive Services for Healthcare Providers 376 14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 377 14.6 Future Directions for Extending Heathcare Services Using CATs 380 14.7 Addressing CAT Challenges in Healthcare as a General Framework 384 14.8 Conclusion 384 References 385 Index 391

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Book SynopsisBrings Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, to open up the story to a modern view of global linked data. Examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data.Table of Contents Preface What is the Semantic Web? Semantic modeling RDF—the basis of the Semantic Web Semantic Web application architecture Linked data Querying the Semantic Web—SPARQL Extending RDF: RDFS and SCHACL RDF Schema RDFS-Plus Using RDFS-Plus in the wild SKOS—managing vocabularies with RDFS-Plus Basic OWL Counting and sets in OWL Ontologies on the Web—putting it all together Good and bad modeling practices Expert modeling in OWL Conclusions and future work Bibliography

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Book SynopsisBrings Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, to open up the story to a modern view of global linked data. Examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data.Table of Contents Preface What is the Semantic Web? Semantic modeling RDF—the basis of the Semantic Web Semantic Web application architecture Linked data Querying the Semantic Web—SPARQL Extending RDF: RDFS and SCHACL RDF Schema RDFS-Plus Using RDFS-Plus in the wild SKOS—managing vocabularies with RDFS-Plus Basic OWL Counting and sets in OWL Ontologies on the Web—putting it all together Good and bad modeling practices Expert modeling in OWL Conclusions and future work Bibliography

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Book Synopsis.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors/Agents/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor TexTable of Contents.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors/Agents/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor Text.- Annotation.- Annotation-based Image Retrieval.- Anomaly Detection on Streams.- Anonymity.- ANSI/INCITS RBAC Standard.- Answering Queries Using Views.- Anti-monotone Constraints.- Applicability Period.- Application Benchmark.- Application Recovery.- Application Server.- Application-Level Tuning.- Applications of Emerging Patterns for Microarray Gene Expression Data Analysis.- Applications of Sensor Network Data Management.- Approximate Queries in Peer-to-Peer Systems.- Approximate Query Processing.- Approximate Reasoning.- Approximation of Frequent Itemsets.- Apriori Property and Breadth-First Search Algorithms.- Architecture-Conscious Database System.- Archiving Experimental Data.- Armstrong Axioms.- Array Databases.- Array Databases_old.- Association Rule Mining on Streams.- Association Rules.- Asymmetric Encryption.- Atelic Data.- Atomic Event.- Atomicity.- Audio.- Audio Classification.- Audio Content Analysis.- Audio Metadata.- Audio Representation.- Audio Segmentation.- Auditing and Forensic Analysis.- Authentication.- Automatic Image Annotation.- Autonomous Replication.- Average Precision.- Average Precision at n.- Average Precision Histogram.- Average R-Precision.- B+-Tree.- Backup and Restore.- Bag Semantics.- Bagging.- Bayesian Classification.- Benchmark Frameworks.- Benchmarks for Big Data Analytics.- Big Data Platforms for Data Analytics.- Big Stream Systems.- Biological Metadata Management.- Biological Networks.- Biological Resource Discovery.- Biological Sequences.- Biomedical Data/Content Acquisition, Curation.- Biomedical Image Data Types and Processing.- Biomedical Scientific Textual Data Types and Processing.- Biostatistics and Data Analysis.- Bi-Temporal Indexing.- Bitemporal Interval.- Bitemporal Relation.- Bitmap Index.- Bitmap-based Index Structures.- Blind Signatures.- Bloom Filters.- BM25.- Boolean Model.- Boosting.- Bootstrap.- Boyce-Codd Normal Form.- BP-Completeness.- Bpref.- Browsing.- Browsing in Digital Libraries.- B-Tree Locking.- Buffer Management.- Buffer Manager.- Buffer Pool.- Business Intelligence.- Business Process Execution Language.- Business Process Management.- Business Process Modeling Notation.- Business Process Reengineering.- Cache-Conscious Query Processing.- Calendar.- Calendric System.- CAP Theorem.- Cardinal Direction Relationships.- Cartesian Product.- Cataloging in Digital Libraries.- Causal Consistency.- Certain (and Possible) Answers.- Change Detection on Streams.- Channel-Based Publish/Subscribe.- Chart.- Chase.- Checksum and Cyclic Redundancy Check Mechanism.- Choreography.- Chronon.- Citation.- Classification.- Classification by Association Rule Analysis.- Classification in Streams.- Client-Server Architecture.- Clinical Data Acquisition, Storage and Management.- Clinical Data and Information Models.- Clinical Data Quality and Validation.- Clinical Decision Support.- Clinical Document Architecture.- Clinical Event.- Clinical Knowledge Repository.- Clinical Observation.- Clinical Ontologies.- Clinical Order.- Closed Itemset Mining and Non-redundant Association Rule Mining.- Closest-Pair Query.- Cloud Computing.- Cloud Intelligence.- Cluster and Distance Measure.- Clustering for Post Hoc Information Retrieval.- Clustering on Streams.- Clustering Overview and Applications.- Clustering Validity.- Clustering with Constraints.- Collaborative Filtering.- Column Segmentation.- Column Stores.- Common Warehouse Metamodel.- Comparative Visualization.- Compensating Transactions.- Complex Event.- Complex Event Processing.- Composed Services and WS-BPEL.- Composite Event.- Composition.- Comprehensions.- Compression of Mobile Location Data.- Computational Media Aesthetics.- Computationally Complete Relational Query Languages.- Computerized Physician Order Entry.- Conceptual Modeling Foundations.- Conceptual Schema Design.- Concurrency Control - Traditional Approaches.- Concurrency Control for Replicated Databases.- Concurrency Control Manager.- Conditional Tables.- Conjunctive Query.- Connection.- Consistency Models For Replicated Data.- Consistent Query Answering.- Constraint Databases.- Constraint Query Languages.- Constraint-Driven Database Repair.- Content-and-Structure Query.- Content-Based Publish/Subscribe.- Content-Based Video Retrieval.- Content-Only Query.- Context.- Contextualization in Structured Text Retrieval.- Continuous Data Protection.- Continuous Monitoring of Spatial Queries.- Continuous Multimedia Data Retrieval.- Continuous Queries in Sensor Networks.- Continuous Query.- ConTract.- Control Data.- Convertible Constraints.- Coordination.- Copyright Issues in Databases.- CORBA.- Correctness Criteria Beyond Serializability.- Cost and quality trade-offs in crowdsourcing.- Cost Estimation.- Count-Min Sketch.- Coupling and De-coupling.- Covering Index.- Crash Recovery.- Cross-Language Mining and Retrieval.- Cross-Modal Multimedia Information Retrieval.- Cross-Validation.- Crowd Database Operators.- Crowd Database Systems.- Crowd Mining and Analysis.- Crowdsourcing Geographic Information Systems.- Cube.- Cube Implementations.- Current Semantics.- Curse of Dimensionality.- Daplex.- Data Acquisition and Dissemination in Sensor Networks.- Data Aggregation in Sensor Networks.- Data Broadcasting, Caching and Replication in Mobile Computing.- Data Cleaning.- Data Compression in Sensor Networks.- Data Conflicts.- Data Definition.- Data Definition Language (DDL).- Data Dictionary.- Data Encryption.- Data Estimation in Sensor Networks.- Data Exchange.- Data Fusion.- Data Fusion in Sensor Networks.- Data Generation.- Data Governance.- Data Integration Architectures and Methodology for the Life Sciences.- Data Integration in Web Data Extraction System.- Data Management for VANETs.- Data Management Fundamentals: Database Management System.- Data Management in Data Centers.- Data Manipulation.- Data Manipulation Language (DML).- Data Mart.- Data Migration Management.- Data Mining.- Data Partitioning.- Data Privacy and Patient Consent.- Data Profiling.- Data Provenance.- Data Quality Assessment.- Data Quality Dimensions.- Data Quality Models.- Data Rank/Swapping.- Data Reduction.- Data Replication.- Data Sampling.- Data Scrubbing.- Data Sketch/Synopsis.- Data Skew.- Data Storage and Indexing in Sensor Networks.- Data Stream.- Data Stream Management Architectures and Prototypes.- Data Types in Scientific Data Management.- Data Uncertainty Management in Sensor Networks.- Data Visualization.- Data Warehouse.- Data Warehouse Life-Cycle and Design.- Data Warehouse Maintenance, Evolution and Versioning.- Data Warehouse Metadata.- Data Warehouse Security.- Data Warehousing for Clinical Research.- Data Warehousing in Cloud Environments.- Data Warehousing on Non-Conventional Data.- Data Warehousing Systems: Foundations and Architectures.- Data, Text, and Web Mining in Healthcare.- Database.- Database Adapter and Connector.- Database Administrator (DBA).- Database Appliances.- Database Benchmarks.- Database Clustering Methods.- Database Clusters.- Database Dependencies.- Database Design.- Database Languages for Sensor Networks.- Database Machine.- Database Management System.- Database Middleware.- Database Repair.- Database Reverse Engineering.- Database Schema.- Database Security.- Database System.- Database Techniques to Improve Scientific Simulations.- Database Trigger.- Database Tuning using Combinatorial Search.- Database Tuning using Online Algorithms.- Database Tuning using Trade-off Elimination.- Database Use in Science Applications.- Datalog.- DBMS Component.- DBMS Interface.- DCE.- DCOM.- Decay Models.- Decision Rule Mining in Rough Set Theory.- Decision Tree Classification.- Decision Trees.- Declarative Networking.- Deductive Data Mining using Granular Computing.- Deduplication.- Deduplication in Data Cleaning.- Deep Instantiation.- Deep-Web Search.- Dense Index.- Dense Pixel Displays.- Density-based Clustering.- Description Logics.- Design for Data Quality.- Dewey Decimal System.- Diagram.- Difference.- Differential Privacy.- Digital Archives and Preservation.- Digital Curation.- Digital Elevation Models.- Digital Libraries.- Digital Rights Management.- Digital Signatures.- Dimension.- Dimension Reduction Techniques for Clustering.- Dimensionality Reduction.- Dimensionality Reduction Techniques For Nearest Neighbor Computations.- Dimension-Extended Topological Relationships.- Direct Attached Storage.- Direct Manipulation.- Disaster Recovery.- Disclosure Risk.- Discounted Cumulated Gain.- Discovery.- Discrete Wavelet Transform and Wavelet Synopses.- Discretionary Access Control.- Disk.- Disk Power Saving.- Distortion Techniques.- Distributed Architecture.- Distributed Concurrency Control.- Distributed Data Streams.- Distributed Database Design.- Distributed Database Systems.- Distributed DBMS.- Distributed Deadlock Management.- Distributed File Systems.- Distributed Hash Table.- Distributed Join.- Distributed Machine Learning.- Distributed Query Optimization.- Distributed Query Processing.- Distributed Recovery.- Distributed Spatial Databases.- Distributed Transaction Management.- Divergence from Randomness Models.- D-measure.- Document.- Document Clustering.- Document Databases.- Document Field.- Document Length Normalization.- Document Links and Hyperlinks.- Document Representations (Inclusive Native and Relational).- Dublin Core.- Dynamic Graphics.- Dynamic Web Pages.- eAccessibility.- ECA Rule Action.- ECA Rule Condition.- ECA Rules.- e-Commerce Transactions.- Effectiveness Involving Multiple Queries.- Ehrenfeucht-Fraïssé Games.- Elasticity.- Electronic Dictionary.- Electronic Encyclopedia.- Electronic Health Record.- Electronic Ink Indexing.- Electronic Newspapers.- Eleven Point Precision-recall Curve.- Emergent Semantics.- Emerging Pattern Based Classification.- Emerging Patterns.- Energy Efficiency in Data Centers.- Ensemble.- Enterprise Application Integration.- Enterprise Content Management.- Enterprise Service Bus.- Enterprise Terminology Services.- Entity Relationship Model.- Entity Resolution.- Entity Retrieval.- Equality-Generating Dependencies.- ERR- Expected Reciprocal Rank.- ERR-IA Intent-aware ERR.- Escrow Transactions.- European Law in Databases.- Evaluation Metrics for Structured Text Retrieval.- Evaluation of Relational Operators.- Event.- Event and Pattern Detection over Streams.- Event Causality.- Event Channel.- Event Cloud.- Event Detection.- Event Driven Architecture.- Event Flow.- Event in Active Databases.- Event in Temporal Databases.- Event Lineage.- Event Pattern Detection.- Event Prediction.- Event Processing Agent.- Event Processing Network.- Event Sink.- Event Source.- Event Specification.- Event Stream.- Event Transformation.- Event-Driven Business Process Management.- Eventual Consistency.- Evidence Based Medicine.- Executable Knowledge.- Execution Skew.- Explicit Event.- Exploratory Data Analysis.- Expressive Power of Query Languages.- Extended Entity-Relationship Model.- Extended Transaction Models and the ACTA Framework.- Extendible Hashing.- Extraction, Transformation, and Loading.- Faceted Search.- Fault-Tolerance and High Availability in Data Stream Management Systems.- Feature Extraction for Content-Based Image Retrieval.- Feature Selection for Clustering.- Feature-Based 3D Object Retrieval.- Field-Based Information Retrieval Models.- Field-Based Spatial Modeling.- First-Order Logic: Semantics.- First-Order Logic: Syntax.- Fixed Time Span.- Flex Transactions.- FM Synopsis.- F-Measure.- Focused Web Crawling.- FOL Modeling of Integrity Constraints (Dependencies).- Forever.- Form.- Fourth Normal Form.- FQL.- Fractal.- Frequency Moments.- Frequent Graph Patterns.- Frequent Items on Streams.- Frequent Itemset Mining with Constraints.- Frequent Itemsets and Association Rules.- Frequent Partial Orders.- Fully-Automatic Web Data Extraction.- Functional Data Model.- Functional Dependencies for Semi-Structured Data.- Functional Dependency.- Functional Query Language.- Fuzzy Models.- Fuzzy Relation.- Fuzzy Set.- Fuzzy Set Approach.- Fuzzy/Linguistic IF-THEN Rules and Linguistic Descriptions.- Gazetteers.- Gene Expression Arrays.- Generalization of ACID Properties.- Generalized Search Tree.- Genetic Algorithms.- Geographic Information System.- Geographical Information Retrieval.- Geography Markup Language.- Geometric Stream Mining.- GEO-RBAC Model.- Georeferencing.- Geosocial Networks.- Geospatial Metadata.- Geo-Targeted Web Search.- GMAP.- Grammar Inference.- Graph.- Graph Data Management in Scientific Applications.- Graph Database.- Graph Management in the Life Sciences.- Graph Mining.- Graph Mining on Streams.- Graph OLAP.- Graphical Models for Uncertain Data Management.- Grid and Workflows.- Grid File (and Family).- GUIs for Web Data Extraction.- Hash Functions.- Hash Join.- Hash-based Indexing.- Healthcare Metrics.- Hierarchial Clustering.- Hierarchical Data Model.- Hierarchical Data Summarization.- Hierarchical Heavy Hitter Mining on Streams.- Hierarchy.- High Dimensional Indexing.- Histogram.- Histograms on Streams.- History in Temporal Databases.- Homomorphic Encryption.- Horizontally Partitioned Data.- Human Factors Modeling in Crowdsourcing.- Human-centered Computing: Application to Multimedia.- Human-Computer Interaction.- Hypertexts.- I/O Model of Computation.- Icon.- Iconic Displays.- Image.- Image Content Modeling.- Image Database.- Image Management for Biological Data.- Image Metadata.- Image Querying.- Image Representation.- Image Retrieval and Relevance Feedback.- Image Segmentation.- Image Similarity.- Implementation of Database Operators (Joins, Group by, etc.).- Implication of Constraints.- Implications of Genomics for Clinical Informatics.- Implicit Event.- Incomplete Information.- Inconsistent Databases.- Incremental Computation of Queries.- Incremental Crawling.- Incremental Maintenance of Views with Aggregates.- Index Creation and File Structures.- Index Join.- Index Structures for Biological Sequences.- Index Tuning.- Indexed Sequential Access Method.- Indexing and Similarity Search.- Indexing Compressed Text.- Indexing Historical Spatio-Temporal Data.- Indexing in pub/sub systems.- Indexing Metric Spaces.- Indexing of Data Warehouses.- Indexing of the Current and Near-Future Positions of Moving Objects.- Indexing Techniques for Multimedia Data Retrieval.- Indexing the Web.- Indexing Uncertain Data.- Indexing Units of Structured Text Retrieval.- Indexing with Crowds.- Individually Identifiable Data.- Inference Control in Statistical Databases.- Information Extraction.- Information Filtering.- Information Foraging.- Information Integration.- Information Integration Techniques for Scientific Data.- Information Lifecycle Management.- Information Loss Measures.- Information Navigation.- Information Quality.- Information Quality and Decision Making.- Information Quality Assessment.- Information Quality Policy and Strategy.- Information Quality: Managing Information as a Product.- Information Retrieval.- Information Retrieval Models.- Information Retrieval Operations.- Infrastructure As-A-Service (IaaS).- Initiative for the Evaluation of XML Retrieval.- Initiator.- In-Network Query Processing.- Integrated DB and IR Approaches.- Integration of Rules and Ontologies.- Intelligent Storage Systems.- Interactive Analytics in Social Media.- Interface.- Interface Engines in Healthcare.- Interoperability in Data Warehouses.- Interoperation of NLP-based Systems with Clinical Databases.- Inter-Operator Parallelism.- Inter-Query Parallelism.- Intra-operator Parallelism.- Intra-Query Parallelism.- Intrusion Detection Technology.- Inverse Document Frequency.- Inverted Files.- IP Storage.- Iterator.- Java Database Connectivity.- Java Enterprise Edition.- Java Metadata Facility.- Join.- Join Dependency.- Join Index.- Join Order.- k-Anonymity.- Karp-Luby Sampling.- KDD Pipeline.- Key.- K-Means and K-Medoids.- Knowledge Base.- Knowledge Base Extraction.- Language Models.- Languages for Web Data Extraction.- Learning Distance Measures.- Lexical Analysis of Textual Data.- Licensing and Contracting Issues in Databases.- Lifespan.- Lightweight Ontologies.- Linear Hashing.- Linear Regression.- Linked Open Data.- Linking and Brushing.- Load Balancing in Peer-to-Peer Overlay Networks.- Load Shedding.- LOC METS.- Locality.- Locality of Queries.- Location Based Recommendation.- Location Management in Mobile Environments.- Location Update Management.- Location-Based Services.- Locking Granularity and Lock Types.- Logging and Recovery.- Logging/Recovery Subsystem.- Logical and Physical Data Independence.- Logical Database Design: from Conceptual to Logical Schema.- Logical Document Structure.- Logical Foundations of Web Data Extraction.- Logical Models of Information Retrieval.- Logical Unit Number.- Logical Unit Number Mapping.- Logical Volume Manager.- Log-Linear Regression.- Loop.- Loose Coupling.- Machine Learning in Computational Biology.- Main Memory.- Main Memory DBMS.- Maintenance of Materialized Views with Outer-Joins.- Maintenance of Recursive Views.- Managing Compressed Structured Text.- Managing Data Integration Uncertainty.- Managing Probabilistic Entity Extraction.- Mandatory Access Control.- MANET Databases.- MAP.- Map Matching.- MapReduce.- Markup Language.- MashUp.- Massive Array of Idle Disks.- Matrix Masking.- Max-Pattern Mining.- Mean Reciprocal Rank.- Measure.- Mediation.- Membership Query.- Memory Hierarchy.- Memory Locality.- Merkle Trees.- Message Authentication Codes.- Message Queuing Systems.- Meta Data Repository.- Meta Object Facility.- Metadata.- Metadata Interchange Specification.- Metadata Registry, ISO/IEC 11179.- Metamodel.- Metasearch Engines.- Metric Space.- Microaggregation.- Microbenchmark.- Microdata.- Microdata Rounding.- Middleware Support for Database Replication and Caching.- Middleware Support for Precise Failure Semantics.- Mining of Chemical Data.- Mobile Database.- Mobile Interfaces.- Mobile resource search.- Mobile Sensor Network Data Management.- Model Management.- Model-based Querying in Sensor Networks.- Monotone Constraints.- Monte Carlo Methods for Uncertain Data.- Moving Object.- Moving Objects Databases and Tracking.- MRR.- Multi-Data Center Consistency Properties.- Multi-Data Center Replication Protocols.- Multidimensional Data Formats.- Multidimensional Modeling.- Multidimensional Scaling.- Multi-Level Modeling.- Multi-Level Recovery and the ARIES Algorithm.- Multilevel Secure Database Management System.- Multilevel Transactions and Object-Model Transactions.- Multimedia Data.- Multimedia Data Buffering.- Multimedia Data Indexing.- Multimedia Data Querying.- Multimedia Data Storage.- Multimedia Databases.- Multimedia Information Retrieval Model.- Multimedia Metadata.- Multimedia Presentation Databases.- Multimedia Resource Scheduling.- Multimedia Retrieval Evaluation.- Multimedia Tagging.- Multimodal Interfaces.- Multi-Pathing.- Multiple Representation Modeling.- Multi-Query Optimization.- Multi-Resolution Terrain Modeling.- Multi-Step Query Processing.- Multitenancy.- Multi-Tier Architecture.- Multi-tier Storage Systems.- Multivalued Dependency.- Multivariate Visualization Methods.- Multi-version Serializability and Concurrency Control.- Naive Tables.- Narrowed Extended XPath I.- Natural Interaction.- Near-duplicate Retrieval.- Nearest Neighbor Classification.- Nearest Neighbor Query.- Nearest Neighbor Query in Spatio-temporal Databases.- Nested Loop Join.- Nested Transaction Models.- Network Attached Secure Device.- Network Attached Storage.- Network Data Model.- Neural Networks.- N-Gram Models.- Noise Addition.- Nonparametric Data Reduction Techniques.- Non-Perturbative Masking Methods.- Non-relational Streams.- Nonsequenced Semantics.- Normal Form ORA-SS Schema Diagrams.- Normal Forms and Normalization.- NoSQL Stores.- Now in Temporal Databases.- Null Values.- OASIS.- Object Constraint Language.- Object Data Models.- Object Identity.- Object Recognition.- Object Relationship Attribute Data Model for Semi-structured Data.- Object Storage Protocol.- Object-Role Modeling.- OLAM.- OLAP Personalization and Recommendation.- OLAP Personalization and Recommendation_old.- One-Copy-Serializability.- One-Pass Algorithm.- On-Line Analytical Processing.- Online Recovery in Parallel Database Systems.- Ontologies and Life Science Data Management.- Ontology.- Ontology Elicitation.- Ontology Engineering.- Ontology Visual Querying.- Ontology-Based Data Access and Integration.- Open Database Connectivity.- Open Information Extraction.- Open Nested Transaction Models.- Operator-Level Parallelism.- Opinion Mining.- Optimistic Replication and Resolution.- Optimization and Tuning in Data Warehouses.- OQL.- Orchestration.- Order Dependency.- OR-Join.- OR-Split.- OSQL.- Outlier Detection.- Overlay Network.- OWL: Web Ontology Language.- P/FDM.- Parallel and Distributed Data Warehouses.- Parallel Coordinates.- Parallel Data Placement.- Parallel Database Management.- Parallel Hash Join, Parallel Merge Join, Parallel Nested Loops Join.- Parallel Query Execution Algorithms.- Parallel Query Optimization.- Parallel Query Processing.- Parameterized Complexity of Queries.- Parametric Data Reduction Techniques.- Partial Replication.- Path Query.- Pattern-Growth Methods.- Peer Data Management System.- Peer to Peer Overlay Networks: Structure, Routing and Maintenance.- Peer-To-Peer Content Distribution.- Peer-to-Peer Data Integration.- Peer-to-Peer Publish-Subscribe Systems.- Peer-to-Peer Storage.- Peer-to-Peer System.- Peer-to-Peer Web Search.- Performance Analysis of Transaction Processing Systems.- Performance Monitoring Tools.- Period-Stamped Temporal Models.- Personalized Web Search.- Petri Nets.- Physical Clock.- Physical Database Design for Relational Databases.- Physical Layer Tuning.- Pipeline.- Pipelining.- Platform As-A-Service (PaaS).- Point-in-Time Copy.- Point-Stamped Temporal Models.- Polytransactions.- Positive Relational Algebra.- Possible Answers.- PRAM.- Precision.- Precision and Recall.- Precision at n.- Precision-Oriented Effectiveness Measures.- Predictive Analytics.- Preference Queries.- Preference Specification.- Prescriptive Analytics.- Presenting Structured Text Retrieval Results.- Primary Index.- Principal Component Analysis.- Privacy.- Privacy Metrics.- Privacy Policies and Preferences.- Privacy through Accountability.- Privacy-Enhancing Technologies.- Privacy-Preserving Data Mining.- Privacy-Preserving DBMSs.- Private Information Retrieval.- Probabilistic Databases.- Probabilistic Entity Resolution.- Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model.- Probabilistic Skylines.- Probabilistic Spatial Queries.- Probabilistic Temporal Databases.- Probability Ranking Principle.- Probability Smoothing.- Process Life Cycle.- Process Mining.- Process Modeling.- Process Optimization.- Process Structure of a DBMS.- Processing Overlaps in Structured Text Retrieval.- Processing Structural Constraints.- Processor Cache.- Profiles and Context for Structured Text Retrieval.- Projection.- Propagation-based Structured Text Retrieval.- Protection from Insider Threats.- Provenance.- Provenance and Reproducibility.- Provenance in Databases.- Provenance in Scientific Databases.- Provenance in Workflows.- Provenance Management.- Provenance Standards.- Provenance Storage.- Provenance: Privacy and Security.- Pseudonymity.- Publish/Subscribe.- Publish/Subscribe over Streams.- Punctuations.- Q-measure.- Quadtrees (and Family).- Qualitative Temporal Reasoning.- Quality and Trust of Information Content and Credentialing.- Quality of Data Warehouses.- Quantiles on Streams.- Quantitative Association Rules.- QUEL.- Query by Humming.- Query Containment.- Query Evaluation Techniques for Multidimensional Data.- Query Expansion for Information Retrieval.- Query Expansion Models.- Query Language.- Query Languages and Evaluation Techniques for Biological Sequence Data.- Query Languages for the Life Sciences.- Query Load Balancing in Parallel Database Systems.- Query Optimization.- Query Optimization (in Relational Databases).- Query Optimization in Sensor Networks.- Query Plan.- Query Point Movement Techniques for Content-Based Image Retrieval.- Query Processing.- Query Processing (in Relational Databases).- Query Processing and Optimization in Object Relational Databases.- Query Processing in data integration systems.- Query Processing in Data Warehouses.- Query Processing in Deductive Databases.- Query Processing over Uncertain Data.- Query Processor.- Query Rewriting.- Query Rewriting Using Views.- Query Translation.- Quorum Systems.- Randomization Methods to Ensure Data Privacy.- Range Query.- Rank-aware Query Processing.- Ranked XML Processing.- Ranking Functions.- Ranking Views.- Rank-Join.- Rank-Join Indices.- Raster Data Management and Multi-Dimensional Arrays.- RDF Stores.- RDF Technology.- Real and Synthetic Test Datasets.- Real-Time Transaction Processing.- Recall.- Receiver Operating Characteristic.- Recommender Systems.- Record Linkage.- Record Matching.- Redundant Arrays of Independent Disks.- Reference Knowledge.- Region Algebra.- Regulatory Compliance in Data Management.- Relational Algebra.- Relational Calculus.- Relational Model.- Relationships in Structured Text Retrieval.- Relative Time.- Relevance.- Relevance Feedback.- Relevance Feedback for Content-Based Information Retrieval.- Relevance Feedback for Text Retrieval.- Replica Control.- Replica Freshness.- Replicated Data Types.- Replicated Database Concurrency Control.- Replication.- Replication Based on Group Communication.- Replication for Availability and Fault-Tolerance.- Replication for High Availability.- Replication for Paxos.- Replication for Scalability.- Replication in Multi-Tier Architectures.- Replication with Snapshot Isolation.- Reputation and Trust.- Request Broker.- Residuated Lattice.- Resource Allocation Problems in Spatial Databases.- Resource Description Framework.- Resource Description Framework (RDF) Schema (RDFS).- Resource Identifier.- Result Display.- Retrospective Event Processing.- Reverse Nearest Neighbor Query.- Reverse Top-k Queries.- Rewriting Queries using Views.- RMI.- Road Networks.- Rocchio's Formula.- Role Based Access Control.- R-Precision.- R-Tree (and Family).- Rule-based Classification.- Safety and Domain Independence.- Sagas.- Sampling Techniques for Statistical Databases.- SAN File System.- Scalable Decision Tree Construction.- Scheduler.- Scheduling Strategies for Data Stream Processing.- Schema Evolution.- Schema Mapping.- Schema Mapping Composition.- Schema Matching.- Schema Tuning.- Schema Versioning.- Scheme/Ontology Extraction.- Scientific Databases.- Scientific Visualization.- Scientific Workflows.- Score Aggregation.- Screen Scraper.- SCSI Target.- SDC Score.- Search Engine Metrics.- Searching Digital Libraries.- Second Normal Form (2NF).- Secondary Index.- Secure Data Outsourcing.- Secure Database Development.- Secure Multiparty Computation Methods.- Secure Transaction Processing.- Security Services.- Segmentation and Stratification.- Segmentation and Stratification_old.- Selection.- Selectivity Estimation.- Self-Maintenance of Views.- Self-Management Technology in Databases.- Semantic Atomicity.- Semantic Crowd Sourcing.- Semantic Data Integration for Life Science Entities.- Semantic Data Model.- Semantic Matching.- Semantic Modeling and Knowledge Representation for Multimedia Data.- Semantic Modeling for Geographic Information Systems.- Semantic Overlay Networks.- Semantic Social Web.- Semantic Streams.- Semantic Web.- Semantic Web Query Languages.- Semantic Web Services.- Semantics-based Concurrency Control.- Semijoin.- Semijoin Program.- Semi-Structured Data.- Semi-Structured Data Model.- Semi-Structured Database Design.- Semi-Structured Query Languages.- Semi-Supervised Learning.- Sensor Networks.- Sequenced Semantics.- Sequential Patterns.- Serializability.- Serializable Snapshot Isolation.- Service Component Architecture (SCA).- Service Oriented Architecture.- Session.- Shared-Disk Architecture.- Shared-Memory Architecture.- Shared-Nothing Architecture.- Side-Effect-Free View Updates.- Signature Files.- Similarity and Ranking Operations.- Simplicial Complex.- Singular Value Decomposition.- Skyline Queries and Pareto Optimality.- Snapshot Equivalence.- Snapshot Isolation.- Snippet.- Snowflake Schema.- SOAP.- Social Applications.- Social influence.- Social Media Analysis.- Social Media Analytics.- Social Media Harvesting.- Social network analysis.- Social Networks.- Software As-A-Service (SaaS).- Software Transactional Memory.- Software-Defined Storage.- Solid State Drive (SSD).- Sort-Merge Join.- Space-Filling Curves.- Space-Filling Curves for Query Processing.- SPARQL.- Sparse Index.- Spatial and Spatio-Temporal Data Models and Languages.- Spatial and Temporal Data Warehouses .- Spatial Anonymity.- Spatial Data Analysis.- Spatial Data Mining.- Spatial Data Types.- Spatial Datawarehousing.- Spatial Indexing Techniques.- Spatial Join.- Spatial Keyword Search.- Spatial Matching Problems.- Spatial Network Databases.- Spatial Operations and Map Operations.- Spatial Queries in the Cloud.- Spatio-Temporal Data Mining.- Spatio-Temporal Data Types.- Spatio-Temporal Data Warehouses.- Spatiotemporal Interpolation Algorithms.- Spatio-Temporal Selectivity Estimation.- Spatio-Temporal Trajectories.- Specialization and Generalization.- Specificity.- Spectral Clustering.- Split.- Split Transactions.- SQL.- SQL Analytics on Big Data.- SQL Isolation Levels.- SQL-Based Temporal Query Languages.- Stable Distribution.- Stack-based Query Language.- Staged DBMS.- Standard Effectiveness Measures.- Star Index.- Star Schema.- State-based Publish/Subscribe.- Statistical Data Management.- Statistical Disclosure Limitation For Data Access.- Steganography.- Stemming.- Stop-&-go Operator.- Stoplists.- Storage Access Models.- Storage Area Network.- Storage Consolidation.- Storage Devices.- Storage Grid.- Storage Management.- Storage Management Initiative-Specification.- Storage Manager.- Storage Network Architectures.- Storage Networking Industry Association.- Storage of Large Scale Multidimensional Data.- Storage Power Management.- Storage Protection.- Storage Protocols.- Storage Resource Management.- Storage Security.- Storage Virtualization.- Stored Procedure.- Stream Mining.- Stream Models.- Stream Processing.- Stream processing on modern hardware.- Stream Reasoning.- Stream Sampling.- Stream Similarity Mining.- Streaming Analytics.- Streaming Applications.- Stream-Oriented Query Languages and Operators.- Strong Consistency Models for Replicated Data.- Structural Indexing.- Structure Analytics in Social Media.- Structure Weight.- Structured Data in Peer-to-Peer Systems.- Structured Document Retrieval.- Structured Text Retrieval Models.- Subject Spaces.- Subspace Clustering Techniques.- Success at n.- Succinct Constraints.- Suffix Tree.- Summarizability.- Summarization.- Support Vector Machine.- Supporting Transaction Time Databases.- Symbolic Representation.- Symmetric Encryption.- Synopsis Structure.- Synthetic Microdata.- System R (R*) Optimizer.- Table.- Tabular Data.- Taxonomy: Biomedical Health Informatics.- tBench.- Telic Distinction in Temporal Databases.- Telos.- Temporal Access Control.- Temporal Aggregation.- Temporal Algebras.- Temporal Analytics in Social Media.- Temporal Benchmarks.- Temporal Coalescing.- Temporal Compatibility.- Temporal Conceptual Models.- Temporal Constraints.- Temporal Data Mining.- Temporal Data Models.- Temporal Database.- Temporal Datawarehousing.- Temporal Dependencies.- Temporal Element.- Temporal Expression.- Temporal Generalization.- Temporal Granularity.- Temporal Homogeneity.- Temporal Indeterminacy.- Temporal Integrity Constraints.- Temporal Joins.- Temporal Logic in Database Query Languages.- Temporal Logical Models.- Temporal Object-Oriented Databases.- Temporal Periodicity.- Temporal Projection.- Temporal PSM.- Temporal Query Languages.- Temporal Query Processing.- Temporal Relational Calculus.- Temporal Specialization.- Temporal Strata.- Temporal Support in the SQL Standard.- Temporal Vacuuming.- Temporal Visual Languages.- Temporal XML.- Term Proximity.- Term Statistics for Structured Text Retrieval.- Term Weighting.- Test Collection.- Text Analytics.- Text Analytics in Social Media.- Text Categorization.- Text Clustering.- Text Compression.- Text Generation.- Text Index Compression.- Text Indexing and Retrieval.- Text Indexing Techniques.- Text Mining.- Text Mining of Biological Resources.- Text Representation.- Text Segmentation.- Text Semantic Representation.- Text Stream Processing.- Text Streaming Model.- Text Summarization.- Text Visualization.- TF*IDF.- Thematic Map.- Third Normal Form.- Three-Dimensional GIS and Geological Applications.- Three-Phase Commit.- Tight Coupling.- Time Aggregated Graphs.- Time and Information Retrieval.- Time Domain.- Time in Philosophical Logic.- Time Instant.- Time Interval.- Time Period.- Time Series Query.- Time Span.- Time-Line Clock.- Timeslice Operator.- Topic Detection and Tracking.- Topic Maps.- Topic-based Publish/Subscribe.- Top-k Queries.- Top-K Selection Queries on Multimedia Datasets.- Topological Data Models.- Topological Relationships.- Trajectory.- Transaction.- Transaction Chopping.- Transaction Management.- Transaction Manager.- Transaction Models - the Read/Write Approach.- Transaction Time.- Transactional Middleware.- Transactional Processes.- Transactional Stream Processing.- Transaction-Time Indexing.- Tree-based Indexing.- Treemaps.- Triangular Norms.- Triangulated Irregular Network.- Trie.- Trip Planning Queries.- Trust and Reputation in Peer-to-Peer Systems.- Trust in Blogosphere.- Trusted Hardware.- TSQL2.- Tuning Concurrency Control.- Tuple-Generating Dependencies.- Two-Dimensional Shape Retrieval.- Two-Phase Commit.- Two-Phase Commit Protocol.- Two-Phase Locking.- Two-Poisson model.- Type-based Publish/Subscribe.- U-measure.- Uncertain Data Lineage.- Uncertain Data Mining.- Uncertain Data Models.- Uncertain Data Streams.- Uncertain Data Summarization.- Uncertain Graph Data Management.- Uncertain Spatial Data Management.- Uncertain Top-k Queries.- Uncertainty in Events.- Uncertainty Management in Scientific Database Systems.- Unicode.- Unified Modeling Language.- Union.- Unobservability.- Updates and Transactions in Peer-to-Peer Systems.- Updates through Views.- Usability.- User-Defined Time.- Valid Time.- Valid-Time Indexing.- Value Equivalence.- Variable Time Span.- Vector-Space Model.- Vertically Partitioned Data.- Video.- Video Content Analysis.- Video Content Modeling.- Video Content Structure.- Video Metadata.- Video Querying.- Video Representation.- Video Scene and Event Detection.- Video Segmentation.- Video Sequence Indexing.- Video Shot Detection.- Video Summarization.- View Adaptation.- View Definition.- View Maintenance.- View Maintenance Aspects.- View-based Data Integration.- Views.- Virtual Partitioning.- Visual Analytics.- Visual Association Rules.- Visual Classification.- Visual Clustering.- Visual Content Analysis.- Visual Data Mining.- Visual Formalisms.- Visual Interaction.- Visual Interfaces.- Visual Interfaces for Geographic Data.- Visual interfaces for streaming data.- Visual Metaphor.- Visual On-Line Analytical Processing (OLAP).- Visual Perception.- Visual Query Language.- Visual Representation.- Visualization for Information Retrieval.- Visualization Pipeline.- Visualizing Categorical Data.- Visualizing Clustering Results.- Visualizing Hierarchical Data.- Visualizing Network Data.- Visualizing Quantitative Data.- Volume.- Voronoi Diagrams.- W3C.- WAN Data Replication.- Wavelets on Streams.- Weak Consistency Models for Replicated Data.- Weak Equivalence.- Web 2.0/3.0.- Web Advertising.- Web Characteristics and Evolution.- Web Crawler Architecture.- Web Data Extraction System.- Web ETL.- Web Harvesting.- Web Information Extraction.- WEB Information Retrieval Models.- Web Mashups.- Web Page Quality Metrics.- Web Question Answering.- Web Search Query Rewriting.- Web Search Relevance Feedback.- Web Search Relevance Ranking.- Web Search Result Caching and Prefetching.- Web Search Result De-duplication and Clustering.- Web Services.- Web Services and the Semantic Web for Life Science Data.- Web Spam Detection.- Web Transactions.- Web Views.- What-If Analysis.- WIMP Interfaces.- Window operator in RDBMS.- Window-based Query Processing.- Windows.- Workflow Constructs.- Workflow Evolution.- Workflow Join.- Workflow Management.- Workflow Management and Workflow Management System.- Workflow Management Coalition.- Workflow Model.- Workflow Model Analysis.- Workflow Patterns.- Workflow Schema.- Workflow Transactions.- Wrapper Induction.- Wrapper Maintenance.- Wrapper Stability.- Write Once Read Many.- XML.- XML Access Control.- XML Attribute.- XML Benchmarks.- XML Compression.- XML Document.- XML Element.- XML Indexing.- XML Information Integration.- XML Integrity Constraints.- XML Metadata Interchange.- XML Metadata Interchange Specification (XMI).- XML Parsing, SAX/DOM.- XML Process Definition Language.- XML Programming.- XML Publish/Subscribe.- XML Publishing.- XML Retrieval.- XML Schema.- XML Selectivity Estimation.- XML Storage.- XML Stream Processing.- XML Tree Pattern, XML Twig Query.- XML Tuple Algebra.- XML Typechecking.- XML Types.- XML Updates.- XML Views.- XPath/XQuery.- XQuery Full-Text.- XQuery Processors.- XSL/XSLT.- Zero-One Laws.- Zooming Techniques.- α-nDCG.-

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Book SynopsisBeginning-Intermediate user levelTable of ContentsChapter 1: Introducing Power BI Chapter 2: Importing Data into Power BI Desktop Chapter 3: Data Munging with Power Query Chapter 4: Creating the Data Model Chapter 5: Creating Calculations with DAX Chapter 6: Creating Measures with DAX Chapter 7: Incorporating Time Intelligence Chapter 8: Creating Reports with Power BI Desktop Chapter 9: Publishing Reports and Creating Dashboards in the Power BI Portal Chapter 10: Introducing Power Pivot in Excel Chapter 11: Data Analysis with Pivot Tables and Charts Chapter 12: Creating a Complete Solution Chapter 13: Advanced Topics in Power Query Chapter 14: Advanced Topics in Power BI DesktopChapter 15: Advanced Topics in Power BI Data Modeling

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Book Synopsis Deliver eye-catching and insightful business intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features of Microsoft''s continually evolving visualization product. New in this edition is help with storytelling-adapted to PCs, tablets, and smartphones-and the building of a data narrative. You will find coverage of templates and JSON style sheets, data model annotations, and the use of composite data sources. Also provided is an introduction to incorporating Python visuals and the much awaited Decomposition Tree visual. Pro Power BI Desktop shows you how to use source data to produce stunning dashboards and compelling reports that you mold into a data narrative to seize your audience''s attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Convert raw data into clear, accurTable of Contents1. Discovering and Loading Data with Power BI Desktop2. Discovering and Loading File-Based Data with Power BI Desktop3. Discovering and Loading File-Based Data with Power BI Desktop4. DirectQuery and Connect Live5. Loading Data from the Web and the Cloud6. Loading Data from Other Data Sources7. Structuring Imported Data8. Data Transformation and Cleansing9. Restructuring Data10. Complex Data Loads11. Organizing, Managing, and Parameterizing Queries12. The M Language13. Creating a Data Model14. Table Visuals15. Matrix and Card Visuals16. Charts in Power BI Desktop17. Formatting Charts in Power BI Desktop18. Other Types of Visuals19. Third-Party Visuals20. Maps in Power BI Desktop21. Filtering Data22. Using Slicers23. Enhancing Dashboards24. Advanced Dashboarding Techniques25. Appendix A: Sample Data

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Book SynopsisBuild a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fastfast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and AzTable of Contents1. The Rise of the Modern Data Warehouse2. The SQL Engine3. The Integration Engine4. The Ingestion Architecture5. The Role of the Data Lake6. The Role of the Data Contract7. Logging, Auditing, and Resilience8. Using Scripting & Automation9. Beyond the Modern Data Warehouse

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Book SynopsisImplement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes.Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. What You Will Learn Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Trade Review“The book is primarily a user’s guide to the NM DEV commercial software library … .” (Anthony J. Duben, Computing Reviews, December 6, 2022)Table of ContentsTable of ContentsAbout the Authors...........................................................................................................iPreface............................................................................................................................ii1. Why Java?..............................................................................................................61.1. Java in 2020.....................................................................................................61.2. Java vs. C++....................................................................................................61.3. Java vs. Python................................................................................................61.4. Java in the future .............................................................................................62. Data Structures.......................................................................................................72.1. Function...........................................................................................................72.2. Polynomial ......................................................................................................73. Linear Algebra .......................................................................................................83.1. Vector and Matrix ...........................................................................................83.1.1. Vector Properties .....................................................................................83.1.2. Element-wise Operations.........................................................................83.1.3. Norm ........................................................................................................93.1.4. Inner product and angle ...........................................................................93.2. Matrix............................................................................................................103.3. Determinant, Transpose and Inverse.............................................................103.4. Diagonal Matrices and Diagonal of a Matrix................................................103.5. Eigenvalues and Eigenvectors.......................................................................103.5.1. Householder Tridiagonalization and QR Factorization Methods..........103.5.2. Transformation to Hessenberg Form (Nonsymmetric Matrices)...........104. Finding Roots of Single Variable Equations .......................................................114.1. Bracketing Methods ......................................................................................114.1.1. Bisection Method ...................................................................................114.2. Open Methods...............................................................................................114.2.1. Fixed-Point Method ...............................................................................114.2.2. Newton’s Method (Newton-Raphson Method) .....................................114.2.3. Secant Method .......................................................................................114.2.4. Brent’s Method ......................................................................................115. Finding Roots of Systems of Equations...............................................................125.1. Linear Systems of Equations.........................................................................125.2. Gauss Elimination Method............................................................................125.3. LU Factorization Methods ............................................................................125.3.1. Cholesky Factorization ..........................................................................125.4. Iterative Solution of Linear Systems.............................................................125.5. System of Nonlinear Equations.....................................................................126. Curve Fitting and Interpolation............................................................................146.1. Least-Squares Regression .............................................................................146.2. Linear Regression..........................................................................................146.3. Polynomial Regression..................................................................................146.4. Polynomial Interpolation...............................................................................146.5. Spline Interpolation .......................................................................................147. Numerical Differentiation and Integration...........................................................157.1. Numerical Differentiation .............................................................................157.2. Finite-Difference Formulas...........................................................................157.3. Newton-Cotes Formulas................................................................................157.3.1. Rectangular Rule....................................................................................157.3.2. Trapezoidal Rule....................................................................................157.3.3. Simpson’s Rules.....................................................................................157.3.4. Higher-Order Newton-Coles Formulas..................................................157.4. Romberg Integration .....................................................................................157.4.1. Gaussian Quadrature..............................................................................157.4.2. Improper Integrals..................................................................................158. Numerical Solution of Initial-Value Problems....................................................168.1. One-Step Methods.........................................................................................168.2. Euler’s Method..............................................................................................168.3. Runge-Kutta Methods...................................................................................168.4. Systems of Ordinary Differential Equations.................................................169. Numerical Solution of Partial Differential Equations..........................................179.1. Elliptic Partial Differential Equations...........................................................179.1.1. Dirichlet Problem...................................................................................179.2. Parabolic Partial Differential Equations........................................................179.2.1. Finite-Difference Method ......................................................................179.2.2. Crank-Nicolson Method.........................................................................179.3. Hyperbolic Partial Differential Equations.....................................................1710..................................................................................................................................1811..................................................................................................................................1912. Random Numbers and Simulation ....................................................................2012.1. Uniform Distribution .................................................................................2012.2. Normal Distribution...................................................................................2012.3. Exponential Distribution............................................................................2012.4. Poisson Distribution ..................................................................................2012.5. Beta Distribution........................................................................................2012.6. Gamma Distribution ..................................................................................2012.7. Multi-dimension Distribution ....................................................................2013. Unconstrainted Optimization ............................................................................2113.1. Single Variable Optimization ....................................................................2113.2. Multi Variable Optimization .....................................................................2114. Constrained Optimization .................................................................................2214.1. Linear Programming..................................................................................2214.2. Quadratic Programming ............................................................................2214.3. Second Order Conic Programming............................................................2214.4. Sequential Quadratic Programming...........................................................2214.5. Integer Programming.................................................................................2215. Heuristic Optimization......................................................................................2315.1. Genetic Algorithm .....................................................................................2315.2. Simulated Annealing .................................................................................2316. Basic Statistics..................................................................................................2416.1. Mean, Variance and Covariance................................................................2416.2. Moment......................................................................................................2416.3. Rank...........................................................................................................2417. Linear Regression .............................................................................................2517.1. Least-Squares Regression..........................................................................2517.2. General Linear Least Squares....................................................................2518. Time Series Analysis ........................................................................................2618.1. Univariate Time Series..............................................................................2618.2. Multivariate Time Series ...........................................................................2618.3. ARMA .......................................................................................................2618.4. GARCH .....................................................................................................2618.5. Cointegration .............................................................................................2619. Bibliography .....................................................................................................2720. Index .....................................................................................................

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Book SynopsisLearn the fundamentals of SAP Enterprise Project and Portfolio management Project Systems (PS), Portfolio and Project Management (PPM) and Commercial Project Management (CPM) and their integration with other SAP modules. This book covers various business scenarios from different industries including the public sector, engineering and construction, professional services, telecom, mining, chemical, and pharmaceutical.Author Joseph Alexander Soosaimuthu will help you understand common business challenges and pain areas faced in portfolio, program and project management, and will provide suitable recommendations to overcome these challenges. This book not only suggests solutions within SAP, but also provides workarounds or integrations with third-party tools based on various Industry-specific business requirements.SAP Portfolio and Project Management addresses commonly asked questions regarding SAP EPPM implementation and deployment, and conveys a framework to facilTable of ContentsChapter 1: Project, Program and Portfolio Management - FundamentalsChapter Goal: To familiarise project, program and portfolio management structures, which subsequent chapters are based on. This chapter will act as building block for further concepts discussed in this book. Sub -Topics 1. Enterprise and Organisation Structure 2. Project Work Breakdown Structure 3. Portfolio and Program Structure 4. Synchronisation of Project, Program and Portfolio Structures 5. Prioritisation Framework Chapter 2: Project Life Cycle – Concept to Closure Chapter Goal: This chapter discusses in detail the various functionalities that will be used during the lifecycle of the project. Sub - Topics 1. Project Planning, Forecasting and Budgeting 2. Project Variation Management 3. Project Commentary 4. Project Issue, Risk and Action item Registers 5. Project Procurement 6. Project Resourcing 7. Project Billing 8. Project Capitalisation 9. Project Closure Chapter 3: Integration Chapter Goal: This chapter will cover critical integration touch points with 3rd party application and also other modules within SAP. Sub - Topics: 1. Detailed level planning of dates and schedules planning with integration to procurement and resourcing. 2. Integration with Schedule Management Applications such as MS project and Oracle Primavera 3. Integration with estimation and costing applications. 4. Integration with Forecasting Application or Excel Integration.Chapter 4: Industry Best Practise and RecommendationChapter Goal: The goal of this chapter is to provide the target audience with insight on business challenges faced during the implementation of Industry best practise and to discuss various solution options with recommendations. Sub - Topics: 1. Industry Best Practise 2. Business Challenges 3. Solution Options 4. Recommendation 5. Commonly asked questions 6. Standard RICEFW List by Industry 7. Standard Functionality List by Industry. Chapter 5: ReportingChapter Goal: This chapter covers reporting related to project, program and portfolio management. It also covers usage of standard ECC and BW Reports/Contents. Sub - Topics: 1. Operational Reporting 2. Month End Reporting 3. Strategic Reporting 4. Long Term Trend Analysis

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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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