Databases / Data management Books

Book SynopsisThe last two decades have seen a wave of exciting new developments in the theory of algorithmic randomness and its applications to other areas of mathematics. This volume surveys much of the recent work that has not been included in published volumes until now. It contains a range of articles on algorithmic randomness and its interactions with closely related topics such as computability theory and computational complexity, as well as wider applications in areas of mathematics including analysis, probability, and ergodic theory. In addition to being an indispensable reference for researchers in algorithmic randomness, the unified view of the theory presented here makes this an excellent entry point for graduate students and other newcomers to the field.Table of Contents1. Key developments in algorithmic randomness Johanna N. Y. Franklin and Christopher P. Porter; 2. Algorithmic randomness in ergodic theory Henry Towsner; 3. Algorithmic randomness and constructive/computable measure theory Jason Rute; 4. Algorithmic randomness and layerwise computability Mathieu Hoyrup; 5. Relativization in randomness Johanna N. Y. Franklin; 6. Aspects of Chaitin's Omega George Barmpalias; 7. Biased algorithmic randomness Christopher P. Porter; 8. Higher randomness Benoit Monin; 9. Resource bounded randomness and its applications Donald M. Stull; Index.

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Book SynopsisBusiness operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team''s performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.Trade Review'This is a timely book for team science, with a unique perspective that uses computational approaches to study the network effect on team performance. The book has a nice balance of theory, algorithms, and empirical studies. The authors possess years of experience in the field.' Charu Aggarwal, IBM Research AI'A comprehensive study that pushes forward our understanding of and ability to forecast and design team performance - a critical, yet complex human-subject phenomenon to which this book brings in-depth technical rigor.' Leman Akoglu, Carnegie Mellon University'This pioneering book is essential to technologists, data scientists, and researchers alike, offering a modern, computational approach to the science of teaming and how to manage the convergence of people, information, and technology in networked organizations.' Norbou Buchler, US Army Data and Analysis Center'Li and Tong have provided a thorough and insightful exploration of current research on teams in networks, linking computational techniques with results from the social sciences. A pleasure to read.' Sucheta Soundarajan, Syracuse University'This brief volume is a valuable resource for managers, but managers with a strong background in data science, and for other technologists involved in designing systems that support user interactions … The added value of this book is provided by the mathematical formalisms used, which encode characteristics of the computational challenges discussed … The topical focus results in a unique volume that might lead interested readers to discover new research avenues … Recommended' J. Brzezinski, ChoiceTable of Contents1. Introduction; 2. Team performance characterization; 3. Team performance prediction; 4. Team performance optimization; 5. Team performance explanation; 6. Human agent teaming; 7. Conclusion and future work.

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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 SynopsisData Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use.Table of ContentsIntroduction xvii Chapter 1 Introduction to Big Data Analytics 1 1.1 Big Data Overview 2 1.1.1 Data Structures 5 1.1.2 Analyst Perspective on Data Repositories 9 1.2 State of the Practice in Analytics 11 1.2.1 BI Versus Data Science 12 1.2.2 Current Analytical Architecture 13 1.2.3 Drivers of Big Data 15 1.2.4 Emerging Big Data Ecosystem and a New Approach to Analytics 16 1.3 Key Roles for the New Big Data Ecosystem 19 1.4 Examples of Big Data Analytics 22 Summary 23 Exercises 23 Bibliography 24 Chapter 2 Data Analytics Lifecycle 25 2.1 Data Analytics Lifecycle Overview 26 2.1.1 Key Roles for a Successful Analytics Project 26 2.1.2 Background and Overview of Data Analytics Lifecycle 28 2.2 Phase 1: Discovery 30 2.2.1 Learning the Business Domain 30 2.2.2 Resources 31 2.2.3 Framing the Problem 32 2.2.4 Identifying Key Stakeholders 33 2.2.5 Interviewing the Analytics Sponsor 33 2.2.6 Developing Initial Hypotheses 35 2.2.7 Identifying Potential Data Sources 35 2.3 Phase 2: Data Preparation 36 2.3.1 Preparing the Analytic Sandbox 37 2.3.2 Performing ETLT 38 2.3.3 Learning About the Data 39 2.3.4 Data Conditioning 40 2.3.5 Survey and Visualize 41 2.3.6 Common Tools for the Data Preparation Phase 42 2.4 Phase 3: Model Planning 42 2.4.1 Data Exploration and Variable Selection 44 2.4.2 Model Selection 45 2.4.3 Common Tools for the Model Planning Phase 45 2.5 Phase 4: Model Building 46 2.5.1 Common Tools for the Model Building Phase 48 2.6 Phase 5: Communicate Results 49 2.7 Phase 6: Operationalize 50 2.8 Case Study: Global Innovation Network and Analysis (GINA) 53 2.8.1 Phase 1: Discovery 54 2.8.2 Phase 2: Data Preparation 55 2.8.3 Phase 3: Model Planning 56 2.8.4 Phase 4: Model Building 56 2.8.5 Phase 5: Communicate Results 58 2.8.6 Phase 6: Operationalize 59 Summary 60 Exercises 61 Bibliography 61 Chapter 3 Review of Basic Data Analytic Methods Using R 63 3.1 Introduction to R 64 3.1.1 R Graphical User Interfaces 67 3.1.2 Data Import and Export 69 3.1.3 Attribute and Data Types 71 3.1.4 Descriptive Statistics 79 3.2 Exploratory Data Analysis 80 3.2.1 Visualization Before Analysis 82 3.2.2 Dirty Data 85 3.2.3 Visualizing a Single Variable 88 3.2.4 Examining Multiple Variables 91 3.2.5 Data Exploration Versus Presentation 99 3.3 Statistical Methods for Evaluation 101 3.3.1 Hypothesis Testing 102 3.3.2 Difference of Means 104 3.3.3 Wilcoxon Rank-Sum Test 108 3.3.4 Type I and Type II Errors 109 3.3.5 Power and Sample Size 110 3.3.6 ANOVA 110 Summary 114 Exercises 114 Bibliography 115 Chapter 4 Advanced Analytical Theory and Methods: Clustering 117 4.1 Overview of Clustering 118 4.2 K-means 118 4.2.1 Use Cases 119 4.2.2 Overview of the Method 120 4.2.3 Determining the Number of Clusters 123 4.2.4 Diagnostics 128 4.2.5 Reasons to Choose and Cautions 130 4.3 Additional Algorithms 134 Summary 135 Exercises 135 Bibliography 136 Chapter 5 Advanced Analytical Theory and Methods: Association Rules 137 5.1 Overview 138 5.2 Apriori Algorithm 140 5.3 Evaluation of Candidate Rules 141 5.4 Applications of Association Rules 143 5.5 An Example: Transactions in a Grocery Store 143 5.5.1 The Groceries Dataset 144 5.5.2 Frequent Itemset Generation 146 5.5.3 Rule Generation and Visualization 152 5.6 Validation and Testing 157 5.7 Diagnostics 158 Summary 158 Exercises 159 Bibliography 160 Chapter 6 Advanced Analytical Theory and Methods: Regression 161 6.1 Linear Regression 162 6.1.1 Use Cases 162 6.1.2 Model Description 163 6.1.3 Diagnostics 173 6.2 Logistic Regression 178 6.2.1 Use Cases 179 6.2.2 Model Description 179 6.2.3 Diagnostics 181 6.3 Reasons to Choose and Cautions 188 6.4 Additional Regression Models 189 Summary 190 Exercises 190 Chapter 7 Advanced Analytical Theory and Methods: Classification 191 7.1 Decision Trees 192 7.1.1 Overview of a Decision Tree 193 7.1.2 The General Algorithm 197 7.1.3 Decision Tree Algorithms 203 7.1.4 Evaluating a Decision Tree 204 7.1.5 Decision Trees in R 206 7.2 Naïve Bayes 211 7.2.1 Bayes’ Theorem 212 7.2.2 Naïve Bayes Classifier 214 7.2.3 Smoothing 217 7.2.4 Diagnostics 217 7.2.5 Naïve Bayes in R 218 7.3 Diagnostics of Classifiers 224 7.4 Additional Classification Methods 228 Summary 229 Exercises 230 Bibliography 231 Chapter 8 Advanced Analytical Theory and Methods: Time Series Analysis 233 8.1 Overview of Time Series Analysis 234 8.1.1 Box-Jenkins Methodology 235 8.2 ARIMA Model 236 8.2.1 Autocorrelation Function (ACF) 236 8.2.2 Autoregressive Models 238 8.2.3 Moving Average Models 239 8.2.4 ARMA and ARIMA Models 241 8.2.5 Building and Evaluating an ARIMA Model 244 8.2.6 Reasons to Choose and Cautions 252 8.3 Additional Methods 253 Summary 254 Exercises 254 Chapter 9 Advanced Analytical Theory and Methods: Text Analysis 255 9.1 Text Analysis Steps 257 9.2 A Text Analysis Example 259 9.3 Collecting Raw Text 260 9.4 Representing Text 264 9.5 Term Frequency—Inverse Document Frequency (TFIDF) 269 9.6 Categorizing Documents by Topics 274 9.7 Determining Sentiments 277 9.8 Gaining Insights 283 Summary 290 Exercises 290 Bibliography 291 Chapter 10 Advanced Analytics—Technology and Tools: MapReduce and Hadoop 295 10.1 Analytics for Unstructured Data 296 10.1.1 Use Cases 296 10.1.2 MapReduce 298 10.1.3 Apache Hadoop 300 10.2 The Hadoop Ecosystem 306 10.2.1 Pig 306 10.2.2 Hive 308 10.2.3 HBase 311 10.2.4 Mahout 319 10.3 NoSQL 322 Summary 323 Exercises 324 Bibliography 324 Chapter 11 Advanced Analytics—Technology and Tools: In-Database Analytics 327 11.1 SQL Essentials 328 11.1.1 Joins 330 11.1.2 Set Operations 332 11.1.3 Grouping Extensions 334 11.2 In-Database Text Analysis 338 11.3 Advanced SQL 343 11.3.1 Window Functions 343 11.3.2 User-Defined Functions and Aggregates 347 11.3.3 Ordered Aggregates 351 11.3.4 MADlib 352 Summary 356 Exercises 356 Bibliography 357 Chapter 12 The Endgame, or Putting It All Together 359 12.1 Communicating and Operationalizing an Analytics Project 360 12.2 Creating the Final Deliverables 362 12.2.1 Developing Core Material for Multiple Audiences 364 12.2.2 Project Goals 365 12.2.3 Main Findings 367 12.2.4 Approach 369 12.2.5 Model Description 371 12.2.6 Key Points Supported with Data 372 12.2.7 Model Details 372 12.2.8 Recommendations 374 12.2.9 Additional Tips on Final Presentation 375 12.2.10 Providing Technical Specifications and Code 376 12.3 Data Visualization Basics 377 12.3.1 Key Points Supported with Data 378 12.3.2 Evolution of a Graph 380 12.3.3 Common Representation Methods 386 12.3.4 How to Clean Up a Graphic 387 12.3.5 Additional Considerations 392 Summary 393 Exercises 394 References and Further Reading 394 Bibliography 394 Index 397

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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 SynopsisUnderstanding Databases: Concepts and Practice is an accessible, highly visual introduction to database systems for undergraduate students across many majors. Designed for self-contained first courses in the subject, this interactive e-textbook covers fundamental database topics including conceptual design, the relational data model, relational algebra and calculus, Structured Query Language (SQL), database manipulation, transaction management, and database design theory. Visual components and self-assessment features provide a more engaging and immersive method of learning that enables students to develop a solid foundation in both database theory and practical application. Concise, easy-to-digest chapters offer ample opportunities for students to practice and master the material, and include a variety of solved real-world problems, self-check questions, and hands-on collaborative activities that task students to build a functioning database. This Enhanced eText also Table of ContentsPreface xi 1 Introduction to Databases and the Relational Data Model 1 1.1 Databases are a Tool 2 1.2 Overview of Data and Models 6 1.3 The Relational Data Model 14 1.3.1 Definition 14 1.3.2 Uniqueness 16 1.3.3 Referential Integrity 20 Special Topic: Primary Key and Referential Integrity 26 1.3.4 Additional Constraints 27 2 Conceptual Design 43 2.1 Gathering Requirements 44 2.2 Entity-Relationship Diagrams 46 How To: Design an Entity-Relationship Diagram 50 Special Topic: (Min, Max) Pairs 54 Special Topic: Recursive Relationships and Role Names 54 Special Topic: Ternary Relationships 56 Special Topic: EER for Modeling Inheritance 56 2.3 Mapping ER Diagrams to Tables 62 How To: Map an ER Diagram to Relations 67 2.4 Other Graphical Approaches 73 3 Relational Algebra 103 3.1 Query Design 104 How To: Query Design 104 3.2 Algebra Operators 109 Note: Overview of Relational Algebra 110 3.2.1 Filtering 111 3.2.2 Sets 114 3.2.3 Joins 116 3.2.4 Division 119 3.3 Relational Completeness 127 3.4 Query Optimization 134 How To: Heuristic Query Optimization 136 4 Relational Calculus 161 4.1 Logical Foundations 162 Note: Overview of Relational Calculus Languages 163 4.2 Tuple Relational Calculus 164 4.2.1 Fundamental Query Expressions 165 How To: Writing a Fundamental Query in TRC 165 vi 4.2.2 Quantification of Variables 167 4.2.3 Atoms and Formula 172 4.2.4 Relational Completeness 175 4.3 Domain Relational Calculus 183 4.3.1 Fundamental Query Expressions 183 How To: Writing a Fundamental Query in DRC 184 4.3.2 Quantification of Variables 187 4.3.3 Atoms and Formula 193 4.3.4 Relational Completeness 195 4.4 Safety 204 5 SQL: An Introduction to Querying 237 5.1 Foundations 237 Note: SQL Syntax 238 Syntax: Basic SQL Query 240 5.2 Fundamental Query Expressions 246 How To: Writing a Fundamental Query in SQL 246 5.2.1 Queries involving One Table 247 5.2.2 Queries involving Multiple Tables 250 How To: Writing a Reflection Query 255 5.3 Nested Queries 261 Special Topic: A glimpse at query optimization 264 Special Topic: Views and Inline Views 266 5.4 Set Operators 270 5.5 Aggregation and Grouping 276 Special Topic: Arithmetic Expressions 281 5.6 Querying with null Values 285 5.7 Relational Completeness 289 5.7.1 Fundamental Operators 290 5.7.2 Additional Operators 292 6 SQL: Beyond the Query Language 329 6.1 Data Definition 329 Syntax: Create Table Statement 331 Syntax: Drop Table Statement 336 Syntax: Alter Table Statement 337 Special Topic: Create Index 338 Syntax: Create View Statement 339 6.2 Data Manipulation 342 Syntax: Insert Into Statement 343 Special Topic: Database Population 346 Syntax: Update Statement 347 Syntax: Delete Statement 349 6.3 Database User Privileges 352 Syntax: Grant Statement 354 Syntax: Revoke Statement 355 7 Database Programming 371 7.1 Persistent Stored Modules 372 Syntax: Create Procedure Statement 374 Syntax: Create Function Statement 376 7.2 Overview of Call-Level Interface 382 7.3 Java and JDBC 385 7.4 Python and DB-API 393 8 XML and Databases 431 8.1 Overview of XML 432 8.2 DTD 439 Syntax: DTD Overview 440 8.3 XML Schema 448 Syntax: XSD Overview of Element and Attribute Declarations 450 Syntax: XSD Attribute Declarations: use, default, fixed 460 8.4 Structuring XML for Data Exchange 467 9 Transaction Management 491 9.1 ACID Properties of a Transaction 492 9.2 Recovery control 498 How To: Recovery Control: UNDO and REDO 501 9.3 Concurrency control 504 9.3.1 Serializability 507 How To: Create a Precedence Graph 508 9.3.2 Locking 512 9.3.3 Timestamps 520 Algorithm: Basic Timestamp Protocol 521 10 More on Database Design 543 10.1 Database Design Goals 544 10.2 Functional Dependencies 546 Algorithm: Attribute Closure 550 Special Topic: Minimal Set of Functional Dependencies 552 How To: Heuristic Determination of a Candidate Key 552 10.3 Decomposition 558 How To: Determine Breakdown of F for a Decomposition 559 How To: Determine Lossless Pairwise Decomposition 562 Algorithm: Lossless-Join Property for Database Schema 565 10.4 Normal Forms 571 How To: Determine the Normal Form of a Relation 574 Algorithm: BCNF Decomposition Algorithm 575 A WinRDBI 599 A.1 Overview 599 A.2 Query Languages 600 A.3 Implementation Overview 606 A.4 Summary 606 Index 607

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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 SynopsisMonetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you'll ever need to lead profitable data science projectsSecret, reverse-engineered data monetization tactics that no one's talking aboutThe shocking truth about how simple natural language processing can beHow to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.Table of ContentsIntroduction 1 Part 1: Getting Started with Data Science 5 Chapter 1: Wrapping Your Head Around Data Science 7 Chapter 2: Tapping into Critical Aspects of Data Engineering 19 Part 2: Using Data Science to Extract Meaning from Your Data 37 Chapter 3: Machine Learning Means Using a Machine to Learn from Data 39 Chapter 4: Math, Probability, and Statistical Modeling 51 Chapter 5: Grouping Your Way into Accurate Predictions 77 Chapter 6: Coding Up Data Insights and Decision Engines 103 Chapter 7: Generating Insights with Software Applications 137 Chapter 8: Telling Powerful Stories with Data 161 Part 3: Taking Stock of Your Data Science Capabilities 187 Chapter 9: Developing Your Business Acumen 189 Chapter 10: Improving Operations 205 Chapter 11: Making Marketing Improvements 229 Chapter 12: Enabling Improved Decision-Making 245 Chapter 13: Decreasing Lending Risk and Fighting Financial Crimes 265 Chapter 14: Monetizing Data and Data Science Expertise 275 Part 4: Assessing Your Data Science Options 289 Chapter 15: Gathering Important Information about Your Company 291 Chapter 16: Narrowing In on the Optimal Data Science Use Case 311 Chapter 17: Planning for Future Data Science Project Success 327 Chapter 18: Blazing a Path to Data Science Career Success 341 Part 5: The Part of Tens 367 Chapter 19: Ten Phenomenal Resources for Open Data 369 Chapter 20: Ten Free or Low-Cost Data Science Tools and Applications 381 Index 397

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Book SynopsisThe information age has led to an explosion in the amount of information available to the individual and the means by which it is accessed, stored, viewed, and transferred. In particular, the growth of the internet has led to the creation of huge repositories of multimedia documents in a diverse range of scientific and professional fields, as well as the tools to extract useful knowledge from them.Mining Multimedia Documents is a must-read for researchers, practitioners, and students working at the intersection of data mining and multimedia applications. It investigates various techniques related to mining multimedia documents based on text, image, and video features. It provides an insight into the open research problems benefitting advanced undergraduates, graduate students, researchers, scientists and practitioners in the fields of medicine, biology, production, education, government, national security and economics.Table of ContentsMining Multimedia Documents: An Overview. Fuzzy Decision Trees for Text Document Clustering. Towards Modeling Semi-Automatic Data Warehouses: Guided by Social Interactions. Multi-Agent System for Text Mining. The transformation of User Requirements in UML Diagrams: An Overview. An Overview of Information Extraction using Textual Case-Based Reasoning. Opinions Classification. Documents Classification Based on Text and Image Features. Content-Based Image Retrieval (CBIR). Mining Knowledge in Medical Image Databases. Segmentation for Medical Image Mining. Biological Data Mining: Techniques and Applications. Video Text Extraction and Mining. Recent Advancement in Multimedia Content using Deep Learning.

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Book SynopsisSince the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more IntrodTrade ReviewPraise for the first edition: "By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."—Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014 "… very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."—William E. Vesely, International Journal of Performability Engineering, July 2013 "Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."—Carl Smith, School of Agriculture and Food Sciences, The University of Queensland "… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. … it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models … readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner "Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."—Neil Cantle, Milliman "The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn’t skim over any of the technical details …"—Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012 "As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."—Michael Corning, Microsoft Corporation "This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples—from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mathematical or statistical background. I highly recommend this book to all researchers, students and practitioners who would like to go beyond traditional statistics or automated data mining techniques and incorporate expert knowledge in their models."—Dr. Lukasz Radlinski, Szczecin University "It is the first book that takes the art and science of developing Bayesian network models for actual problems as seriously as the underlying mathematics. The reader will obtain a good understanding of the methods as they are introduced through well-motivated and intuitive examples and attractive case studies. The authors do this in such a way that readers with little previous exposure to probability theory and statistics will be able to grasp and appreciate the power of Bayesian networks. While this in itself is already a major achievement, the authors go far beyond this by providing very close and pragmatic links between model building and the required techniques. It, thus, shares insights that are mostly missing from other textbooks, making this book also of interest to advanced readers, lecturers and researchers in the area."—Prof.dr. Peter Lucas, Institute for Computing and Information Sciences, Radboud University Nijmegen, and Leiden Institute of Advanced Computer Science, Leiden University "This book gives a thorough account of Bayesian networks, one of the most widely used frameworks for reasoning with uncertainty, and their application in domains as diverse as system reliability modelling and legal reasoning. The book's central premise is that ‘essentially, all models are wrong, but some are useful’ (G.E.P. Box), and the book distinguishes itself by focusing on the art of building useful models for risk assessment and decision analysis rather than on delving into mathematical detail of the models that are built. The authors are renowned for their ability to put Bayesian network technology into practical use, and it is therefore no surprise that the book is filled to the brim with motivating and relevant examples. With the accompanying evaluation copy of the excellent AgenaRisk software, readers can easily play around with the examples and gain valuable insights of how the models behave ‘at work.’ I believe this book should be of interest to practitioners working with risk assessment and decision making and also as a valuable textbook in undergraduate courses on probability and risk."—Helge Langseth, Norwegian University of Science and Technology "Bayesian networks are revolutionizing the way experts assess and manage uncertainty. This is the first book to explain this powerful new tool to a non-specialist audience. It takes us on a compelling journey from the basics of probability to sophisticated networks of system design, finance and crime. This trip is greatly supported by free software, allowing readers to explore and develop Bayesian networks for themselves. The style is accessible and entertaining, without sacrificing conceptual or mathematical rigor. This book is a must-read for anyone wanting to learn about Bayesian networks; it provides the know-how and software so that we can all share this adventure into risk and uncertainty."—David Lagnado, Senior Lecturer in Cognitive and Decision Sciences, University College London "This is the book I have wanted to see for many years. Whilst we are entitled to see appropriate duty of care in any risk management scenario, ill-informed practice is in fact prevalent in industry and society. There is little real excuse for this as classical decision theory has a long established history, and it can now be operationalized in complex scenarios using the Bayesian network technology that is a core theme of this book. The problem has been that most books on Bayesian networks and decision theory focus in depth on the technical foundations, and provide little in the way of practical guidance on how to use the technology to support real-world risk assessment and decision making.In contrast, Norman Fenton and Martin Neil have provided a clearly written and highly readable book that is packed with informative and insightful examples. I had fun reading it, but there is also sufficient technical detail so that one can obtain a deep understanding of the subject from studying the book. It is a joy, and one that I keep dipping back into."—Paul Krause, Professor of Software Engineering, University of Surrey "Given the massive uncertainties managers now need to operate within, this book is both vital and timely. Fenton and Neil’s explanation of how to create practical models that simulate real-life strategic scenarios gives hard-pressed managers a new tool that they can use to understand potential impacts and opportunities. This book should be required reading for anyone involved in strategy, business planning, or significant decision-making."—Rob Wirszycz, Celaton Limited Praise for the first edition: "By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."—Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014 "… very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."—William E. Vesely, International Journal of Performability Engineering, July 2013 "Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."—Carl Smith, School of Agriculture and Food Sciences, The University of Queensland "… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. … it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models … readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner "Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."—Neil Cantle, Milliman "The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn’t skim over any of the technical details …"—Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012 "As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."—Michael Corning, Microsoft Corporation "This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples—from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mathematical or statistical background. I highly recommend this book to all researchers, students and practitioners who would like to go beyond traditional statistics or automated data mining techniques and incorporate expert knowledge in their models."—Dr. Lukasz Radlinski, Szczecin University "It is the first book that takes the art and science of developing Bayesian network models for actual problems as seriously as the underlying mathematics. The reader will obtain a good understanding of the methods as they are introduced through well-motivated and intuitive examples and attractive case studies. The authors do this in such a way that readers with little previous exposure to probability theory and statistics will be able to grasp and appreciate the power of Bayesian networks. While this in itself is already a major achievement, the authors go far beyond this by providing very close and pragmatic links between model building and the required techniques. It, thus, shares insights that are mostly missing from other textbooks, making this book also of interest to advanced readers, lecturers and researchers in the area."—Prof.dr. Peter Lucas, Institute for Computing and Information Sciences, Radboud University Nijmegen, and Leiden Institute of Advanced Computer Science, Leiden University "This book gives a thorough account of Bayesian networks, one of the most widely used frameworks for reasoning with uncertainty, and their application in domains as diverse as system reliability modelling and legal reasoning. The book's central premise is that ‘essentially, all models are wrong, but some are useful’ (G.E.P. Box), and the book distinguishes itself by focusing on the art of building useful models for risk assessment and decision analysis rather than on delving into mathematical detail of the models that are built. The authors are renowned for their ability to put Bayesian network technology into practical use, and it is therefore no surprise that the book is filled to the brim with motivating and relevant examples. With the accompanying evaluation copy of the excellent AgenaRisk software, readers can easily play around with the examples and gain valuable insights of how the models behave ‘at work.’ I believe this book should be of interest to practitioners working with risk assessment and decision making and also as a valuable textbook in undergraduate courses on probability and risk."—Helge Langseth, Norwegian University of Science and Technology "Bayesian networks are revolutionizing the way experts assess and manage uncertainty. This is the first book to explain this powerful new tool to a non-specialist audience. It takes us on a compelling journey from the basics of probability to sophisticated networks of system design, finance and crime. This trip is greatly supported by free software, allowing readers to explore and develop Bayesian networks for themselves. The style is accessible and entertaining, without sacrificing conceptual or mathematical rigor. This book is a must-read for anyone wanting to learn about Bayesian networks; it provides the know-how and software so that we can all share this adventure into risk and uncertainty."—David Lagnado, Senior Lecturer in Cognitive and Decision Sciences, University College London "This is the book I have wanted to see for many years. Whilst we are entitled to see appropriate duty of care in any risk management scenario, ill-informed practice is in fact prevalent in industry and society. There is little real excuse for this as classical decision theory has a long established history, and it can now be operationalized in complex scenarios using the Bayesian network technology that is a core theme of this book. The problem has been that most books on Bayesian networks and decision theory focus in depth on the technical foundations, and provide little in the way of practical guidance on how to use the technology to support real-world risk assessment and decision making.In contrast, Norman Fenton and Martin Neil have provided a clearly written and highly readable book that is packed with informative and insightful examples. I had fun reading it, but there is also sufficient technical detail so that one can obtain a deep understanding of the subject from studying the book. It is a joy, and one that I keep dipping back into."—Paul Krause, Professor of Software Engineering, University of Surrey "Given the massive uncertainties managers now need to operate within, this book is both vital and timely. Fenton and Neil’s explanation of how to create practical models that simulate real-life strategic scenarios gives hard-pressed managers a new tool that they can use to understand potential impacts and opportunities. This book should be required reading for anyone involved in strategy, business planning, or significant decision-making."—Rob Wirszycz, Celaton Limited Table of ContentsThere Is More to Assessing Risk Than Statistics. The Need for Causal, Explanatory Models in Risk Assessment. Measuring Uncertainty: The Inevitability of Subjectivity. The Basics of Probability. Bayes’ Theorem and Conditional Probability. From Bayes’ Theorem to Bayesian Networks. Defining the Structure of Bayesian Networks. Building and Eliciting Node Probability Tables. Numeric Variables and Continuous Distribution Functions. Hypothesis Testing and Confidence Intervals. Modeling Operational Risk. Systems Reliability Modeling. Bayes and the Law. Learning Bayesian Networks. Decision making, Influence Diagrams and Value of information. Bayesian networks in forensics. Using Bayesian networks to debunk bad statistics. Bayesian networks for football prediction. Appendix A: The Basics of Counting. Appendix B: The Algebra of Node Probability Tables. Appendix C: Junction Tree Algorithm. Appendix D: Dynamic Discretization. Appendix E: Statistical Distributions.

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Book SynopsisThe Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years' experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R's capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.

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Book SynopsisDisk-Based Algorithms for Big Data is a product of recent advances in the areas of big data, data analytics, and the underlying file systems and data management algorithms used to support the storage and analysis of massive data collections. The book discusses hard disks and their impact on data management, since Hard Disk Drives continue to be common in large data clusters. It also explores ways to store and retrieve data though primary and secondary indices. This includes a review of different in-memory sorting and searching algorithms that build a foundation for more sophisticated on-disk approaches like mergesort, B-trees, and extendible hashing. Following this introduction, the book transitions to more recent topics, including advanced storage technologies like solid-state drives and holographic storage; peer-to-peer (P2P) communication; large file systems and query languages like Hadoop/HDFS, Hive, Cassandra, and Presto; and NoSQL databases like Neo4j for graph structurTable of ContentsForeword. Physical Disk Storage. File Management. Sorting. Searching. Disk-Based Sorting. Disk-Based Searching. Storage Technology. Large File Systems. NoSQL Storage. Appendix

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Book SynopsisBig data' is now readily available to economic historians, thanks to the digitisation of primary sources, collaborative research linking different data sets, and the publication of databases on the internet. Key economic indicators, such as the consumer price index, can be tracked over long periods, and qualitative information, such as land use, can be converted to a quantitative form. In order to fully exploit these innovations it is necessary to use sophisticated statistical techniques to reveal the patterns hidden in datasets, and this book shows how this can be done.A distinguished group of economic historians have teamed up with younger researchers to pilot the application of new techniques to big data'. Topics addressed in this volume include prices and the standard of living, money supply, credit markets, land values and land use, transport, technological innovation, and business networks. The research spans the medieval, early modern and modern periods. Research methoTrade Review'This book makes applied econometric methods accessible to anyone interested in quantitative economic history' — Helen Paul, University of Southampton, UK.Table of Contents1. Introduction: Research methods for large databases Mark Casson and Nigar Hashimzade 2. Long-run Price Dynamics: The measurement of substitutability between commodities Mark Casson, Nigar Hashimzade and Catherine Casson 3. The Quantity Theory of Money in Historical Perspective Nick Mayhew 4. Medieval Foreign Exchange: A time series analysis Adrian Bell, Chris Brooks and Tony K. Moore 5. Local Property Values in Fourteenth and Fifteenth-century England Margaret Yates, Anna Campbell and Mark Casson 6. Visual Analytics for Large-scale Actor Networks, with an Application to Liverpool Business Networks John Haggerty and Sheryllynne Haggerty 7. Railways and Local Population Growth: Northamptonshire and Rutland, 1801-91 Mark Casson, Leigh Shaw-Taylor, A.E.M. Satchell and E.A. Wrigley 8. Women’s Land Ownership in Nineteenth-century England Janet Casson 9. The Diffusion of Steam Technology in England: Ploughing engines, 1860-1930 Jane McCutchan 10. Industrious Burglars: Funding consumption from property crime Jane Humphries, Sara Horrell and Ken Sneath

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Book SynopsisThis book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issueTable of Contents1 Large-Scale Video Event Detection Using Deep Neural Networks 2 Leveraging Selectional Preferences for Anomaly Detection in Newswire Events 3 Abnormal Event Recognition in Crowd Environments 4 Cognitive Sensing: Adaptive Anomalies Detection with Deep Networks 5 Language-Guided Visual Recognition 6 Deep Learning for Font Recognition and Retrieval 7 A Distributed Secure Machine-Learning Cloud Architecture for Semantic Analysis 8 A Practical Look at Anomaly Detection Using Autoencoders with H2O and the R Programming Language

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Book SynopsisThe development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradiTable of ContentsPrefaceEditorsContributors1 Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition AssessmentAmir H. Alavi and William G. Buttlar2 Global Satellite Observations for Smart CitiesZhong Liu, Menglin S. Jin, Jacqueline Liu, Angela Li, William Teng, Bruce Vollmer, and David Meyer3 Advancing Smart and Resilient Cities with Big Spatial Disaster Data: Challenges, Progress, and OpportunitiesXuan Hu and Jie Gong4 Smart City Portrayal: Dynamic Visualization Applied to the Analysis of Underground MetroEvgheni Polisciuc and Penousal Machado5 Smart Bike-Sharing Systems for Smart CitiesHesham A. Rakha, Mohammed Elhenawy, Huthaifa I. Ashqar, Mohammed H. Almannaa, and Ahmed Ghanem6 Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal ProcessingAbdollah Malekjafarian, Eugene J. OBrien, and Fatemeh Golpayegani7 Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart CitiesArash Jahangiri, Sahar Ghanipoor Machiani, and Vahid Balali8 Exploratory Analysis of Run-Off-Road Crash PatternsMohammad Jalayer, Huaguo Zhou, and Subasish Das9 Predicting Traffic Safety Risk Factors Using an Ensemble ClassifierNasim Arbabzadeh, Mohammad Jalayer, and Mohsen Jafari10 Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public HousesClyde Zhengdao Li, Bo Yu, Cheng Fan, and Jingke HongIndex.

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Book SynopsisData Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes cTable of Contents1. Digital Transformation of Mining. 2. Data Analytics and the Mining Value Chain. 3. Data Collection, Storage and Retrieval. 4. Making Sense of Data. 5. Analytics Toolset. 6. Making Decisions based on Analytics. 7. Process Performance Analytics. 8. Process Maintenance Analytics. 9. Data Analytics for Energy Efficiency and Gas Emission Reduction. 10. Future Skills Requirements.

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Book SynopsisData Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today's information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more.In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, anTrade Review"In the tradition of Edward Tufte’s design strategies for visual and narrative representations that frame our shared reality, Sosulski has artfully crafted a functional guide to design patterns and processes for shaping expressions of data and, most importantly, its usage in real-world organizational contexts. This will be a go-to reference in my library for years to come."—Jason Severs, Chief Design Officer, Droga5Table of Contents1. Becoming Visual 2. The Tools 3. The Graphics 4. The Data 5. The Design 6. The Audience 7. The Presentation 8. The Cases 9. The End

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Book SynopsisThere is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.Table of ContentsBig Data Applications in Lung Research. Artificial convolution neural network techniques and applications for big data of lung for nodule detection. Deep learning with non-medical training used for pathology identification in big data chest images. Unsupervised pre-training across image domains improves lung tissue classification in lung big data sets. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks in big data sets of CT Lungs. Big Data Applications in Colon Research. A comprehensive computer-aided polyp detection system for big data colonoscopy videos. Automatic polyp detection in big data colonoscopy videos using an ensemble of convolutional neural networks. A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations in big data colonoscopy. Off-the-shelf convolutional neural network features for pulmonary nodule detection in big data computed tomography scans. Big Data Applications in Breast Cancer. Mitosis detection in big data breast cancer histology images with deep neural networks. Convolutional neural networks for mass lesion classification in big data mammography. Standard plane localization in fetal ultrasound via domain transferred deep neural networks in large ultrasound data sets. Unregistered multiview analysis with pre-trained deep learning models in large mammographic data sets. Big Data Applications in Brain Imaging. Brain tumor segmentation with deep neural networks using big data sets. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation in big data MRI images. Deep neural networks segment neuronal membranes in electron microscopy images. Alzheimer's Disease Diagnosis by Adaptation of 3D Convolutional Network in large MRI brain images. Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks. Big Data Applications in Heart Imaging. Automating carotid intima-media thickness video interpretation with convolutional neural networks. Interleaved text/image deep mining on a very large-scale radiology database. Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition in big data sets. Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks in large MRI populations. Big Data Applications in Urology and Abdomen Imaging. A New NMF-Autoencoder Based CAD System for Early Diagnosis of Prostate Cancer by considering big data sets. Image-Based Computer-Aided Diagnosis for Early Diagnosis of Prostate Cancer in large data sets. Deep convolutional networks for pancreas segmentation in large scale CT imaging. A Promising Non-invasive CAD System for Kidney Function Assessment.

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Book SynopsisFully updated and expanded from the previous edition, A Practical Guide to Database Design, Second Edition is intended for those involved in the design or development of a database system or application. It begins by illustrating how to develop a Third Normal Form data model where data is placed “where it belongs”. The reader is taken step-by-step through the Normalization process, first using a simple then a more complex set of data requirements. Next, usage analysis for each Logical Data Model is reviewed and a Physical Data Model is produced that will satisfy user performance requirements. Finally, each Physical Data Model is used as input to create databases using both Microsoft Access and SQL Server.The book next shows how to use an industry-leading data modeling tool to define and manage logical and physical data models, and how to create Data Definition Language statements to create or update a database running in SQL Server, OracTable of Contents1. Overview of Databases 2. Normalization 3. Database Implementation 4. Normalization and Physical Design Exercise 5. The erwin Data Modeling Tool 6. Using Microsoft Access 7. Using SQL Server 8. Using Perl to Extract and Load Data 9. Building User Interfaces 10. Creating the University Database Application 11. PHP Implementation and Used

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Book SynopsisUsing data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player''s shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.Features: One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball Presents tools for modelling graphs and figures to visualize the data Includes real woTrade Review"This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball." –Virgilio Gomez Rubio From the forward: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs "Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community." –Patrick Mair, Harvard University "The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans." –Jason Osborne, North Carolina State University "My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport" –Jack Davis, Simon Fraser University "This book I know by heart and like it very much. It is a nice collection of data science methods for basketball analysis combined with software code examples (in the statistical programming language R)."–Prof. Dr. Andreas Groll, Technische Universität Dortmund "This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball." –Virgilio Gomez Rubio From the foreword: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs "Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community." –Patrick Mair, Harvard University "The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans." –Jason Osborne, North Carolina State University "My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport" –Jack Davis, Simon Fraser University “The real strength of this book is that it is meant to be hands-on. As part of the text, the authors provide access to a custom-built package in R, along with an excellent pre-prepared data set (one full season’s worth of NBA box score and play-by-play data). The authors then guide the reader through many examples of building graphs and tables using their R package and data. The graphs are often intricate and visually detailed, but the text shows how to make them quickly, giving detailed instructions. I imagine that a reader looking to get into basketball analysis could find this book very exciting, because it provides a quick and easy entry point into conducting sophisticated analyses and making visually arresting graphs and figures. A reader can easily follow along and replicate everything that is done in the book. Or, what is more likely, the reader can skim through the text until they come to a plot that looks particularly cool, and then by reading the surrounding section they can quickly learn how to do such an analysis for themselves.” –Brian Skinner, MIT "This book I know by heart and like it very much. It is a nice collection of data science methods for basketball analysis combinedwith software code examples (in the statistical programming language R)."–Prof. Dr. Andreas Groll, Technische Universität Dortmund "For those interested in any level of statistical data analysis in basketball, specifically in R, Basketball Data Science: With Applications in R would be a valuable addition to their library. Further, this text would be quite useful for a course in sports data focusing on basketball or for a student’s research project." Russ Goodman, Central College, Iowa, USA, Mathematical Association of America, April 2023. Table of Contents1. Introduction. 2. Finding Groups in Data. 3. Finding Structures in Data with Machine Learning. 4. Modelling Relationships in Basketball. 5. Concluding Remarks and Future Perspectives.

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Book SynopsisIn order to make informed decisions, there are three important elements: intuition, trust, and analytics. Intuition is based on experiential learning and recent research has shown that those who rely on their gut feelings may do better than those who don't. Analytics, however, are important in a data-driven environment to also inform decision making. The third element, trust, is critical for knowledge sharing to take place. These three elementsintuition, analytics, and trustmake a perfect combination for decision making. This book gathers leading researchers who explore the role of these three elements in the process of decision-making.Table of ContentsIntuition. The Underpinnings of Intuition. How Intuition Affects Decision Making. Data, Insights, Models, and Decisions. The Missing Link—Experiential Learning. Cases of Intuition Outperforming Analytics. Trust. The Foundation of Trust. Trust and Organizational Leadership. Trust and Knowledge Sharing. Trust and Organizational Communication. Trust and Marketing. Trust and Social Media. Analytics. The Secret Sauce. Predictive Analytics. Prescriptive Analytics. Developing an Analytics Strategy. Looking Toward the Future with Cognitive Computing and AI.

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Book SynopsisFuzzy logic principles, practices, and real-world applicationsThis hands-on guide offers clear explanations of fuzzy logic along with practical applications and real-world examples. Written by an award-winning engineer, Fuzzy Logic: Applications in Artificial Intelligence, Big Data, and Machine Learning is aimed at improving competence and motivation in students and professionals alike.Inside, you will discover how to apply fuzzy logic in the context of pervasive digitization and big data across emerging technologies which require a very different man-machine relationship than the ones previously used in engineering, science, economics, and social sciences. Applications covered include intelligent energy systems with demand response, smart homes, electrification of transportation, supply chain efficiencies, smart cities, e-commerce, education, healthcare, and decarbonization.Serves as a classroom guide and as an on-the-job resource

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Book SynopsisDelivering concise, cutting-edge coverage, CONCEPTS OF DATABASE MANAGEMENT, 8e uses real-world cases, examples, and illustrations to give students a thorough understanding of such critical issues as database design, data integrity, concurrent updates, data security, and more. Completely updated to Microsoft Access 2013 standards, the text presents SQL in a database-neutral environment and covers all major topics, including E-R diagrams, normalization, and database design. It provides detailed coverage of the relational model (including QBE and SQL), normalization and views, database administration and management, and more. Advanced topics covered include distributed databases, data warehouses, stored procedures, triggers, data macros, and Web Apps. Ideal for an introductory database course in an information systems, business, or CIS program, CONCEPTS OF DATABASE MANAGEMENT, 8e can be used in varying disciplines by instructors who want database coverage without using a trade book or a lTable of Contents1. Introduction to Database Management. 2. The Relational Model 1: Introduction, QBE, and Relational Algebra. 3. The Relational Model 2: SQL. 4. The Relational Model 3: Advanced Topics. 5. Database Design 1: Normalization. 6. Database Design 2: Design Method. 7. DBMS Functions. 8. Database Administration. 9. Database Management Approaches. Appendix A: Comprehensive Design Example: Marvel College. Appendix B: SQL Reference. Appendix C: How Do I" Reference. Appendix D: Answers to Odd-Numbered Review Questions. Appendix E: Access Web Apps. Appendix F: Systems Analysis Approach to Information-Level Requirements."

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Book SynopsisTable of Contents1 Introduction 11.1 Fundamental Concepts . . . . . . . . . . . . . . . . . . . . . 21.2 Access Control Models . . . . . . . . . . . . . . . . . . . . . 191.3 Cryptographic Concepts . . . . . . . . . . . . . . . . . . . . . 251.4 Implementation and Usability Issues . . . . . . . . . . . . . . 391.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Physical Security 552.1 Physical Protections and Attacks . . . . . . . . . . . . . . . . 562.2 Locks and Safes . . . . . . . . . . . . . . . . . . . . . . . . . 572.3 Authentication Technologies . . . . . . . . . . . . . . . . . . . 712.4 Direct Attacks Against Computers . . . . . . . . . . . . . . . 882.5 Special-Purpose Machines . . . . . . . . . . . . . . . . . . . 992.6 Physical Intrusion Detection . . . . . . . . . . . . . . . . . . . 132.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 3 Operating Systems Security 1133.1 Operating Systems Concepts . . . . . . . . . . . . . . . . . . 114 3.2 Process Security . . . . . . . . . . . . . . . . . . . . . . . . . 1303.3 Memory and Filesystem Security . . . . . . . . . . . . . . . . 136 3.4 Application Program Security . . . . . . . . . . . . . . . . . . 1493.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 4 Malware 173 4.1 Insider Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . 1744.2 Computer Viruses . . . . . . . . . . . . . . . . . . . . . . . . 1814.3 Malware Attacks . . . . . . . . . . . . . . . . . . . . . . . . . 1884.4 Privacy-Invasive Software . . . . . . . . . . . . . . . . . . . . 202 4.5 Countermeasures . . . . . . . . . . . . . . . . . . . . . . . . 2084.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 5 Network Security I 2215.1 Network Security Concepts . . . . . . . . . . . . . . . . . . . 2225.2 The Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 2295.3 The Network Layer . . . . . . . . . . . . . . . . . . . . . . . . 2365.4 The Transport Layer . . . . . . . . . . . . . . . . . . . . . . . 2465.5 Denial-of-Service Attacks . . . . . . . . . . . . . . . . . . . . 256 5.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 6 Network Security II 2696.1 The Application Layer and DNS . . . . . . . . . . . . . . . . . 2706.2 Firewalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2876.3 Tunneling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 6.4 Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . 2996.5 Wireless Networking . . . . . . . . . . . . . . . . . . . . . . . 313 6.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 7 Web Security 3277.1 The World Wide Web . . . . . . . . . . . . . . . . . . . . . . 3287.2 Attacks on Clients . . . . . . . . . . . . . . . . . . . . . . . . 347 7.3 Attacks on Servers . . . . . . . . . . . . . . . . . . . . . . . . 3687.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 8 Cryptography 3878.1 Symmetric Cryptography . . . . . . . . . . . . . . . . . . . . 3888.2 Public-Key Cryptography . . . . . . . . . . . . . . . . . . . . . 4068.3 Cryptographic Hash Functions . . . . . . . . . . . . . . . . . 4178.4 Digital Signatures . . . . . . . . . . . . . . . . . . . . . . . . . 4218.5 Details on AES and RSA . . . . . . . . . . . . . . . . . . . . 4258.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 9 Distributed-Applications Security 4879.1 Database

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Book SynopsisFrank M. Carrano is Professor Emeritus of Computer Science at the University of Rhode Island. He received his Ph.D. degree in Computer Science from Syracuse University in 1969. His interests include data structures, computer science education, social issues in computing, and numerical computation. Professor Carrano is particularly interested in the design and delivery of undergraduate courses in computer science. He has authored several well-known computer science textbooks for undergraduates. Timothy M. Henry has a Bachelor of Science Degree in Mathematics from the U.S. Coast Guard Academy, a Master of Science Degree in Computer Science from Old Dominion University, and was awarded a PhD in Applied Math Sciences from the University of Rhode Island. He began his IT career as an officer in the U.S. Coast Guard, and among his early tours, he was the Information Resources Manager (what is today a CIO) at the Coast Guard's training centre in Yorktown, VA.Table of Contents Introduction Chapter 1: Bags Chapter 2: Bag Implementations That Use Arrays Chapter 3: A Bag Implementation That Links Data Chapter 4: The Effciency of Algorithms Chapter 5: Stacks Chapter 6: Stack Implementations Chapter 7: Recursion Chapter 8: An Introduction to Sorting Chapter 9: Faster Sorting Methods Chapter 10: Queues, Deques, and Priority Queues Chapter 11: Queue, Deque, and Priority Queue Implementations Chapter 12: Lists Chapter 13: A List Implementation That Uses an Array Chapter 14: A List Implementation That Links Data Chapter 15: Iterators for the ADT List Chapter 16: Sorted Lists Chapter 17: Inheritance and Lists Chapter 18: Searching Chapter 19: Dictionaries Chapter 20: Dictionary Implementations Chapter 21: Introducing Hashing Chapter 22: Hashing as a Dictionary Implementation Chapter 23: Trees Chapter 25: A Binary Search Tree Implementation Chapter 26: A Heap Implementation Chapter 27: Balanced Search Trees Chapter 28: Graphs Chapter 29: Graph Implementations

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Book SynopsisDavid Kroenke has many years of teaching experience at Colorado State University, Seattle University, and the University of Washington. He has led dozens of seminars for college professors on the teaching of information systems and technology; in 1991, the International Association of Information Systems named him Computer Educator of the Year. In 2009, David was named Educator of the Year by the Association of Information Technology Professionals-Education Special Interest Group (AITP-EDSIG). David worked for the US Air Force and Boeing Computer Services. He was a principal in the startup of three companies, serving as the vice president of product marketing and development for the Microrim Corporation and as chief of database technologies for Wall Data, Inc. He is the father of the semantic object data model. David's consulting clients have included IBM, Microsoft, and Computer Sciences Corporations, as well as numerous smaller companies. Recently, David hTable of ContentsPart I: Getting Started 1. Introduction 2. Introduction to Structured Query Language Part II: Database Design 3.The Relational Model and Normalization 4. Database Design Using Normalization 5. Data Modeling and the Entity-Relationship Model 6. Transforming Data Models in Database Designs Part III: Database Implementation 7.SQL for Database Construction and Application Processing 8. Database Redesign Part IV: Multiuser Database Processing 9. Managing Multiuser Databases 10.Managing Databases with SQL Server 2014, Oracle Database 12c, and MySQL 5.7 Online Chapter: 10A.Managing Databases with SQL Server 2014 Online Chapter: 10B.Managing Databases with Oracle 12c Online Chapter: 10C.Managing Databases with MySQL 5.7 Part V: Database Access Standards 11. The Web Server Environment 12. Big Data, Data Warehouses, and Business Intelligence Systems Online Appendix A. Getting Started with Microsoft Access 2013 Online Appendix B. Getting Started in Systems Analysis and Design Online Appendix C. E-R Diagrams and the IDEF1X Standard Online Appendix D. E-R Diagrams and the UML Standard Online Appendix E. Getting Started with MySQL Workbench Data Modeling Tools Online Appendix F. Getting Started with Microsoft Vision 2013 Online Appendix G. Data Structures for Database Processing Online Appendix H. The Semantic Object Model Online Appendix I. Getting Started with Web Servers, PHP and the Eclipse PDT Online Appendix J. Business Intelligence Systems Online Appendix K. Big Data

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Book SynopsisGain a thorough, applied understanding of critical database issues with Starks/Pratt/Last's CONCEPTS OF DATABASE MANAGEMENT, 9E. Real cases, examples and screenshots in this concise presentation help clarify database design, data integrity, normalization, concurrent updates, data security, and big data. Completely updated to SQL Server 2016, Microsoft Access 2016, and Office 365 standards, this edition explores SQL in a database-neutral environment while addressing E-R diagrams, normalization, and database design. Detailed coverage presents the relational model (including QBE and SQL), normalization and views, database administration and management. You also examine advanced topics such as distributed databases, data warehouses, stored procedures, triggers, data macros and Web Apps. This introduction to database is ideal for mastering today's database techniques.Table of Contents1. Introduction to Database Management. 2. The Relational Model 1: Introduction, QBE, and Relational Algebra. 3. The Relational Model 2: SQL. 4. The Relational Model 3: Advanced Topics. 5. Database Design 1: Normalization. 6. Database Design 2: Design Method. 7. DBMS Functions. 8. Database Administration. 9. Database Management Approaches. Appendix A: Comprehensive Design Example: Marvel College. Appendix B: SQL Reference. Appendix C: MySQL. Appendix D: How Do I" Reference. Appendix E: Using Access to Create a Web App. Appendix F: A Systems Analysis Approach to Information-Level Requirements."

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Book SynopsisIntroducing SQLite.- Getting Started.- SQL for SQLite.- Advanced SQL for SQLite.- SQLite Design and Concepts.- The Core C API.- The Extension C API.- Language Extensions.- iOS Development with SQLite.- Android Development with SQLite.- SQLite Internals and New Features.Table of Contents Introducing SQLite Getting Started SQL for SQLite Advanced SQL for SQLite SQLite Design and Concepts The Core C API The Extension C API Language Extensions iOS Development with SQLite Android Development with SQLite SQLite Internals and New Features

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Book SynopsisEntity Framework 6 Recipes provides an exhaustive collection of ready-to-use code solutions for Entity Framework, Microsoft's model-centric, data-access platform for the .NET Framework and ASP.NET development.Table of Contents Getting Started with Entity Framework Entity Data Modeling Fundamentals Querying an Entity Data Model Using Entity Framework in ASP.NET Loading Entities and Navigation Properties Beyond the Basics with Modeling and Inheritance Working with Object Services Plain Old CLR Objects Using the Entity Framework in N-Tier Applications Stored Procedures Functions Customizing Entity Framework Objects Improving Performance Concurrency

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Book SynopsisBeginning Oracle SQL is your introduction to the interactive query tools and specific dialect of SQL used with Oracle Database.Table of Contents1. Relational Database Systems and Oracle2. Introduction to SQL and SQL*Plus, and SQL Developer3. Data Definition, Part I4. Retrieval: The Basics5. Retrieval: Functions6. Data Manipulation7. Data Definition, Part II8. Retrieval: Joins and Grouping9. Retrieval: Advanced Features10. Views11. Automating12. Object-Relational Features13. Appendix A – Case Tables14. Appendix B – Exercise Solutions

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Book SynopsisAn emerging topic in software engineering and data mining, specification mining tackles software maintenance and reliability issues that cost economies billions of dollars each year. The first unified reference on the subject, Mining Software Specifications: Methodologies and Applications describes recent approaches for mining specifications of software systems. Experts in the field illustrate how to apply state-of-the-art data mining and machine learning techniques to address software engineering concerns.In the first set of chapters, the book introduces a number of studies on mining finite state machines that employ techniques, such as grammar inference, partial order mining, source code model checking, abstract interpretation, and more. The remaining chapters present research on mining temporal rules/patterns, covering techniques that include path-aware static program analyses, lightweight rule/pattern mining, statistical analysis, and other interesting apTable of ContentsSpecification Mining: A Concise Introduction. Mining Finite-State Automata with Annotations. Adapting Grammar Inference Techniques to Mine State Machines. Mining API Usage Protocols from Large Method Traces. Static API Specification Mining: Exploiting Source Code Model Checking. Static Specification Mining Using Automata-Based Abstractions. DynaMine: Finding Usage Patterns and Their Violations by Mining Software Repositories. Automatic Inference and Effective Application of Temporal Specifications. Path-Aware Static Program Analyses for Specification Mining. Mining API Usage Specifications via Searching Source Code from the Web. Merlin: Specification Inference for Explicit Information Flow Problems. Lightweight Mining of Object Usage.

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Book SynopsisManaging and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy.Trade ReviewFrom the reviews:“This book provides a survey of some recent advances in graph mining. It contains chapters on graph languages, indexing, clustering, pattern mining, keyword search, and pattern matching. … The book is targeted at advanced undergraduate or graduate students, faculty members, and researchers from both industry and academia. … I highly recommend this book to someone who is starting to explore the field of graph mining or wants to delve deeper into this exciting field.” (Dimitrios Katsaros, ACM Computing Reviews, December, 2010)Table of ContentsAn Introduction to Graph Data.- Graph Data Management and Mining: A Survey of Algorithms and Applications.- Graph Mining: Laws and Generators.- Query Language and Access Methods for Graph Databases.- Graph Indexing.- Graph Reachability Queries: A Survey.- Exact and Inexact Graph Matching: Methodology and Applications.- A Survey of Algorithms for Keyword Search on Graph Data.- A Survey of Clustering Algorithms for Graph Data.- A Survey of Algorithms for Dense Subgraph Discovery.- Graph Classification.- Mining Graph Patterns.- A Survey on Streaming Algorithms for Massive Graphs.- A Survey of Privacy-Preservation of Graphs and Social Networks.- A Survey of Graph Mining for Web Applications.- Graph Mining Applications to Social Network Analysis.- Software-Bug Localization with Graph Mining.- A Survey of Graph Mining Techniques for Biological Datasets.- Trends in Chemical Graph Data Mining.

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Book SynopsisThe enthusiasm behind Node doesn't just reflect the promise of server-side JavaScript. Developers also have the potential to create elegant applications with this open source framework that are much easier to maintain.

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Book SynopsisHow can you take advantage of feedback control for enterprise programming? With this book, author Philipp K. Janert demonstrates how the same principles that govern cruise control in your car also apply to data center management and other enterprise systems.

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Book SynopsisWith this practical book, you will learn proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets.

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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 SynopsisAmbient Diagnostics addresses innovative methods for discovering patterns from affordable devices, such as mobile phones, watches, cameras, and game interfaces, to interpret multimedia data for personal health monitoring and diagnosis. This is the first comprehensive textbook on multidisciplinary innovations in affordable healthcarefrom sensory fusion, pattern detection, to classification.Connecting the DotsThe material in this book combines sensing, pattern recognition, and visual design, and is divided into four parts, which cover fundamentals, multimedia intelligence, pervasive sensors, and crowdsourcing. The author describes basic pattern discovery models, sound, color, motion and video analytics, and pattern discovery from games and social networks. Each chapter contains the material's main concepts, as well as case studies, and extensive study questions. Trade Review"... not only the first book of its kind, it's the only reference I know of that shows how designers and engineers might create a new world of ambient healthcare with non-invasive, low-cost and accessible sensors. Yang Cai's book is a very readable, extremely comprehensive collection of technologies and applications for readily accessible sensors and mobile technologies for immediate application in everyday diagnosis across the spectrum of health situations. Yang Cai's book is especially valuable as a deeply-researched and reliable source for engineers, product and medical device designers to quickly learn and adapt the emerging tools and sensors to facilitate what is so brilliantly framed as "ambient diagnostics." -Peter Jones, PhD, OCAD University, Toronto "The main contribution of this book is a comprehensive transversal approach that combines this ample background into a single matter ... a combination of diverse methodologies and strategies that are directly applicable to sense, perceive and recognize a great diversity of ambient parameters. Its easy-to-read style and its frequent references to real life examples make the book very attractive to the reader." -Julio Abascal, University of the Basque Country/Euskal Herriko UnibertsitateaTable of Contents Part I. Fundamentals. Introduction. Transformation. Pattern Recognition. Part II. Multimedia Intelligence. Sound Recognition. Color Sensors. Kinect Sensors. Video Analytics. Fatigue Sensing. Part III. Pervasive Sensors. Mobile Sensors. Body Media. Pocket Microscopes. Personal Spectrometers. Part IV. Crowd Sourcing. Remote Sensing. Games for Diagnosis. Social Media. Problems. Sample Source Code. Further Readings. Index.

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Book SynopsisThe Shakespearean Inside' is a study of all soliloquies and solo asides (dubbed insides for short) in Shakespeare's complete plays.

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