Databases / Data management Books

Book SynopsisPresent energy systems are undergoing a radical transformation, driven by the urgent need to address the climate change crisis. At the same time, we are witnessing the sharp growth of energy data and a revolution of advanced technologies, with artificial intelligence (AI) and Blockchain emerging as two of the most transformative technologies of our time. The convergence of these two technologies has the potential to create a paradigm shift in the energy sector, enabling the development of smart energy systems that are more resilient, efficient, and sustainable.This book situates itself at the forefront of this paradigm shift, providing a timely and comprehensive guide to AI and Blockchain technologies in the energy system. Moving from an introduction to the basic concepts of smart energy systems, this book proceeds to examine the key challenges facing the energy system, and how AI and Blockchain can be used to address these challenges. Research examples are presented to showcTable of ContentsList of FiguresList of TablesForewordPrefaceAuthor BiosContributorsSection I Fundamental TheoriesChapter 1 Smart Energy SystemsChapter 2 Theories of Artificial IntelligenceChapter 3 Theories of Blockchain TechnologiesSection II Applications in Smart Energy SystemsChapter 4 Reforms in Energy Systems: Prosumers Era and Future Low Carbon Energy SystemsChapter 5 Application of Artificial Intelligence for Energy SystemsChapter 6 Implementation of Blockchain in Local Energy MarketsChapter 7 Cyber Physical System Modeling for Energy InternetSection III Testbeds for Smart Energy SystemsChapter 8 Developing Testbeds for Smart Energy Systems

Read more less

Book SynopsisNew computational design tools have evolved rapidly and been increasingly applied in the field of design in recent years, complimenting and even replacing the traditional design media and approaches. Design as both the process and product are changing due to the emergence and adoption of these new technologies. Understanding and assessing the impact of these new computational design environments on design and designers is important for advancing design in the contemporary context. Do these new computational environments support or hinder design creativity? How do those tools facilitate designers' thinking? Such knowledge is also important for the future development of design technologies. Research shows that design is never a mysterious non-understandable process, for example, one general view is that design process shares a common analysis-synthesis-evaluation model, during which designers interact between design problem and solution spaces. Understanding designers' thinking in difTable of ContentsIntroduction. Emergent technologies in computational design. Understanding design cognition in computational and generative design. Cognitive impacts and computational design environments. Conclusion.

Read more less

Book SynopsisData Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences.Key Features:â Elucidation of data scientific workflow. â Introduction to reproducible research. â In-depth coverage of data-scientifTable of Contents1. Bienvenue! 2. Getting Started 3. Why Data Science? 4. Data Manipulation 5. Data Visualization 6. Automated Reporting 7. Example Project: The Biscuit Study 8. Data Collection 9. Data Preparation 10. Data Analysis 11. Value Delivery 12. Machine Learning 13. Text Analysis 14. Dashboards 15. Conclusion and Next Steps

Read more less

Book SynopsisAPCOM is a peer-reviewed forum for industrial and research communities working in the mineral industry to share expertise on the application of computer and operations research technology. Recognized since the 1960s as the world's premier conference in the field, APCOM features an impressive range of topics from geostatistics to data warehousing. APCOM 2005 builds on this reputation, showcasing the latest industrial applications and emerging technologies, focusing particularly on mobilizing the inherent value in largely under-used data and information systems, and how these data systems cab be analyzed for real-time or process-based improvements.

Read more less

Book SynopsisData warehousing is one of the hottest business topics, and there's more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition.Table of ContentsIntroduction 1 Part I: The Data Warehouse: Home for Your Data Assets 7 Chapter 1: What’s in a Data Warehouse? 9 Chapter 2: What Should You Expect from Your Data Warehouse? 25 Chapter 3: Have It Your Way: The Structure of a Data Warehouse 37 Chapter 4: Data Marts: Your Retail Data Outlet 59 Part II: Data Warehousing Technology 71 Chapter 5: Relational Databases and Data Warehousing 73 Chapter 6: Specialty Databases and Data Warehousing 85 Chapter 7: Stuck in the Middle with You: Data Warehousing Middleware 95 Part III: Business Intelligence and Data Warehousing 113 Chapter 8: An Intelligent Look at Business Intelligence 115 Chapter 9: Simple Database Querying and Reporting 125 Chapter 10: Business Analysis (OLAP) 135 Chapter 11: Data Mining: Hi-Ho, Hi-Ho, It’s Off to Mine We Go 149 Chapter 12: Dashboards and Scorecards 155 Part IV: Data Warehousing Projects: How to Do Them Right 163 Chapter 13: Data Warehousing and Other IT Projects: The Same but Different 165 Chapter 14: Building a Winning Data Warehousing Project Team 179 Chapter 15: You Need What? When? — Capturing Requirements 193 Chapter 16: Analyzing Data Sources 203 Chapter 17: Delivering the Goods 213 Chapter 18: User Testing, Feedback, and Acceptance 225 Part V: Data Warehousing: The Big Picture 231 Chapter 19: The Information Value Chain: Connecting Internal and External Data 233 Chapter 20: Data Warehousing Driving Quality and Integration 247 Chapter 21: The View from the Executive Boardroom 263 Chapter 22: Existing Sort-of Data Warehouses: Upgrade or Replace? 271 Chapter 23: Surviving in the Computer Industry (and Handling Vendors) 281 Chapter 24: Working with Data Warehousing Consultants 291 Part VI: Data Warehousing in the Not-Too-Distant Future 297 Chapter 25: Expanding Your Data Warehouse with Unstructured Data 299 Chapter 26: Agreeing to Disagree about Semantics 305 Chapter 27: Collaborative Business Intelligence 311 Part VII: The Part of Tens 317 Chapter 28: Ten Questions to Consider When You’re Selecting User Tools 319 Chapter 29: Ten Secrets to Managing Your Project Successfully 325 Chapter 30: Ten Sources of Up-to-Date Information about Data Warehousing 331 Chapter 31: Ten Mandatory Skills for a Data Warehousing Consultant 335 Chapter 32: Ten Signs of a Data Warehousing Project in Trouble 339 Chapter 33: Ten Signs of a Successful Data Warehousing Project 343 Chapter 34: Ten Subject Areas to Cover with Product Vendors 347 Index 351

Read more less

Book SynopsisLike many other organizing paradigms, smart data strategy isrevolutionary and essential to enterprise performance. SmartData explores smart data strategy to enhance enterpriseperformance. Smart Data provides valuable tools in business,like skills for better enterprise decision-making, enterpriseperformance, and agility towards change.Table of ContentsForeword. Preface. Acknowledgments. Introduction: A Comprehensive Overview. Predictive Management. IDEF Lexicon for Executives. Organization of This Book. Smart Data in Three Dimensions. Business Rule. Case Study: IT Capital Budgeting Using a Knapsack Problem. Case Study: Better Decision Making: Field Testing, Evaluation and Validation of a Web-Based MedWatch Decision Support System (MWDSS). Engineering an Ubiquitous Strategy for Catalyzing Enterprise Performance Optimization. What Smart Data Provides. References. 1 Context: The Case and Place for Smart Data Strategy. 1.1 Value of Data to the Enterprise. 1.2 Enterprise Performance Versus Enterprise Integration. 1.3 Current Problems and Deficiencies from Poor Data Strategy. 1.4 New Technologies. 1.5 Breaking from Tradition with Improved Results. References. 2 Elements: Smart Data and Smart Data Strategy. 2.1 Performance Outcomes and Attributes. 2.2 Policy and Business Rules. 2.3 Expectations: Managerial and Technical. 2.4 Capacity for Change and Improvement. 2.5 Iteration Versus Big Bang. References. 3 Barriers: Overcoming Hurdles and Reaching a New Performance Trajectory. 3.1 Barriers. 3.2 Overcoming Barriers. 3.3 Top–Down Strategy. 3.4 Balance of Consequences and Reinforcement. 3.5 Collaboration. 3.6 Enterprise Performance Optimization Process. 3.7 Enterprise Performance Optimization Architecture. 3.8 Scoping, Scheduling, Budgeting, and Project and Program Management. References. 4 Visionary Ideas: Technical Enablement. 4.1 Today’s Possibilities. 4.2 Calibrating Executive Expectations. 4.3 Five Years from Now. 4.4 Ten Years From Now. References. 5. CEO’s Smart Data Handbook. 5.1 Strategy. 5.2 Policy. 5.3 Organization. 5.4 Actions. 5.5 Timing. 5.6 Funding and Costing Variables. 5.7 Outcomes and Measurements. References. Index. Wiley Series in Systems Engineering and Management.

Read more less

Book SynopsisThis book demonstrates the role of coding in communication and data storage systems design, illustrating the correct use of codes and the selection of the right code parameters. Relevant decoding techniques and their implementation are discussed in detail, while emphasizing the fundamental concepts of coding theory with minimal mathematical tools.Table of ContentsThe Principles of Coding in Digital Communications. Convolutional Codes. Linear Block Codes. Cyclic Codes. Finite Field Arithmetic. BCH Codes. Reed Solomon Codes. Performance Calculations for Block Codes. Multistage Coding. Iterative Decoding. Index.

Read more less

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

Read more less

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

Read more less

Book SynopsisThis large-format, alphabetized password book is organized by tabs per letter, making it easy, fast, and safe to store and locate important login information of all kinds! Individual tabs for each letter—no more tabs cramming multiple letters into the same space! Perfect for faster lookups and better organization. Removable sticker to go incognito! Don''t want text on the cover sharing that it’s a password book? Peel it off! Bonus security tips to encourage maximized online safety. What to do (and what not to do) to stay ahead of scammers. Large trim size for extra space to record over 400 accounts, including important notes, password changes, and non-traditional records such as crypto logins.

Read more less

Book SynopsisJulie C. Meloni is a technical consultant who has been developing web-based applications since the Web first saw the light of day. She has authored numerous books and articles on web-based programming and scripting languages and database topics, and you can find translations of her work in 18 different languages.  Table of ContentsPart I: Web Application Basics CHAPTER 1: Understanding How the Web Works A Brief History of HTML and the World Wide Web Creating Web Content Understanding Web Content Delivery Selecting a Web Hosting Provider Testing with Multiple Web Browsers Creating a Sample File Using FTP to Transfer Files Understanding Where to Place Files on the Web Server CHAPTER 2: Structuring HTML and Using Cascading Style Sheets Getting Started with a Simple Web Page HTML Tags Every Web Page Must Have Using Hyperlinks in Web Pages Organizing a Page with Paragraphs and Line Breaks Organizing Your Content with Headings Understanding Semantic Elements How CSS Works A Basic Style Sheet A CSS Style Primer Using Style Classes Using Style IDs Internal Style Sheets and Inline Styles CHAPTER 3: Understanding the CSS Box Model and Positioning The CSS Box Model The Whole Scoop on Positioning Controlling the Way Things Stack Up Managing the Flow of Text Understanding Fixed Layouts Understanding Fluid Layouts Creating a Fixed/Fluid Hybrid Layout Considering a Responsive Web Design CHAPTER 4: Introducing JavaScript Learning Web Scripting Basics How JavaScript Fits into a Web Page Exploring JavaScript’s Capabilities Basic JavaScript Language Concepts JavaScript Syntax Rules Using Comments Best Practices for JavaScript Understanding JSON Using the JavaScript Console to Debug JavaScript CHAPTER 5: Introducing PHP How PHP Works with a Web Server The Basics of PHP Scripts Code Blocks and Browser Output Part II: Getting Started with Dynamic Websites CHAPTER 6: Understanding Dynamic Websites and HTML5 Applications Refresher on the Different Types of Scripting Displaying Random Content on the Client Side Understanding the Document Object Model Using window Objects Working with the document Object Accessing Browser History Working with the location Object More About the DOM Structure Working with DOM Nodes Creating Positionable Elements (Layers) Hiding and Showing Objects Modifying Text Within a Page Adding Text to a Page Changing Images Based on User Interaction Thinking Ahead to Developing HTML5 Applications CHAPTER 7: JavaScript Fundamentals: Variables, Strings, and Arrays Using Variables Understanding Expressions and Operators Data Types in JavaScript Converting Between Data Types Using String Objects Working with Substrings Using Numeric Arrays Using String Arrays Sorting a Numeric Array CHAPTER 8: JavaScript Fundamentals: Functions, Objects, and Flow Control Using Functions Introducing Objects Using Objects to Simplify Scripting Extending Built-in Objects Using the Math Object Working with Math Methods Working with Dates The if Statement Using Shorthand Conditional Expressions Testing Multiple Conditions with if and else Using Multiple Conditions with switch Using for Loops Using while Loops Using do…while Loops Working with Loops Looping Through Object Properties CHAPTER 9: Understanding JavaScript Event Handling Understanding Event Handlers Using Mouse Events Using Keyboard Events Using the load and unload Events CHAPTER 10: The Basics of Using jQuery Using Third-Party JavaScript Libraries jQuery Arrives on the Scene Preparing to Use jQuery Becoming Familiar with the $().ready Handler Selecting DOM and CSS Content Manipulating HTML Content Putting the Pieces Together to Create a jQuery Animation Handling Events with jQuery Part III: Taking Your Web Applications to the Next Level CHAPTER 11: AJAX: Remote Scripting Introducing AJAX Using XMLHttpRequest Creating a Simple AJAX Library Creating an AJAX Quiz Using the Library Debugging AJAX-Based Applications Using jQuery’s Built-in Functions for AJAX CHAPTER 12: PHP Fundamentals: Variables, Strings, and Arrays Variables Data Types Using Expressions and Operators Constants Understanding Arrays Creating Arrays Some Array-Related Constructs and Functions CHAPTER 13: PHP Fundamentals: Functions, Objects, and Flow Control Calling Functions Defining a Function Returning Values from User-Defined Functions Understanding Variable Scope Saving State Between Function Calls with the static Statement More About Arguments Testing for the Existence of a Function Creating an Object Object Inheritance Switching Flow Implementing Loops CHAPTER 14: Working with Cookies and User Sessions Introducing Cookies Setting a Cookie Deleting a Cookie Overview of Server-Side Sessions Working with Session Variables Destroying Sessions and Unsetting Session Variables Using Sessions in an Environment with Registered Users CHAPTER 15: Working with Web-Based Forms How HTML Forms Work Creating a Form Accepting Text Input Naming Each Piece of Form Data Labeling Each Piece of Form Data Grouping Form Elements Exploring Form Input Controls Using HTML5 Form Validation Submitting Form Data Accessing Form Elements with JavaScript Accessing Form Elements with PHP Using Hidden Fields to Save State in Dynamic Forms Sending Mail on Form Submission Part IV: Integrating a Database into Your Applications CHAPTER 16: Understanding the Database Design Process The Importance of Good Database Design Types of Table Relationships Understanding Normalization Following the Design Process CHAPTER 17: Learning Basic SQL Commands Learning the MySQL Data Types Learning the Table-Creation Syntax Using the INSERT Statement Using the SELECT Statement Using WHERE in Your Queries Selecting from Multiple Tables Using the UPDATE Statement to Modify Records Using the REPLACE Statement Using the DELETE Statement Frequently Used String Functions in MySQL Using Date and Time Functions in MySQL CHAPTER 18: Interacting with MySQL Using PHP MySQL or MySQLi? Connecting to MySQL with PHP Working with MySQL Data Part V: Getting Started with Application Development CHAPTER 19: Creating a Simple Discussion Forum Designing the Database Tables Creating an Include File for Common Functions Creating the Input Forms and Scripts Displaying the Topic List Displaying the Posts in a Topic Adding Posts to a Topic Modifying the Forum Display with JavaScript CHAPTER 20: Creating an Online Storefront Planning and Creating the Database Tables Displaying Categories of Items Displaying Items Using JavaScript with an Online Storefront CHAPTER 21: Creating a Simple Calendar Building a Simple Display Calendar Creating the Calendar in JavaScript CHAPTER 22: Managing Web Applications Understanding Some Best Practices in Web Application Development Writing Maintainable Code Implementing Version Control in Your Work Understanding the Value and Use of Code Frameworks Appendixes APPENDIX A: Installation QuickStart Guide with XAMPP APPENDIX B: Installing and Configuring MySQL APPENDIX C: Installing and Configuring Apache APPENDIX D: Installing and Configuring PHP

Read more less

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

Read more less

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

Read more less

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

Read more less

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

Read more less

Book SynopsisAs Web-based systems and e-commerce carry businesses into the 21st century, databases are becoming workhorses that shoulder each and every online transaction. For organizations to have effective 24/7 Web operations, they need powerhouse databases that deliver at peak performance-all the time. High Performance Web Databases: Design, Development, and Deployment arms you with every essential technique from design and modeling to advanced topics such as data conversion, performance tuning, Web access and interfacing legacy systems, and securityTable of ContentsDatabase Planning and Getting Started. Information Gathering and Analysis. Managing Business Rules. Performance Modeling Methods. Performance Design and Development. Database Integrity and Quality. Distributed Databases, Portability, and Interoperability. Database Integration with the Internet and the Web. Data Migration, Conversion, and Legacy Applications. Performance Tuning. Data Administration and Operations. Data Base Security.

Read more less

Book SynopsisThe explosion of Web-based data has created a demand among executives and technologists for methods to identify, gather, analyze, and utilize data that may be of value to corporations and organizations. The emergence of data mining, and the larger field of Web mining, has businesses lost within a confusing maze of mechanisms and strategies for obtaining and managing crucial intelligence.Web Data Mining and Applications in Business Intelligence and Counter-Terrorism responds by presenting a clear and comprehensive overview of Web mining, with emphasis on CRM and, for the first time, security and counter-terrorism applications. The tools and methods of Web mining are revealed in an easy-to-understand style, emphasizing the importance of practical, hands-on experience in the creation of successful e-business solutions.The author, a program director for Data and Applications Security at the National Science Foundations, details how both opportunities and dangers on the WTable of ContentsIntroduction. SUPPORTING TECHNOLOGIES FOR WEB DATA MINING. The World Wide Web and E-Commerce. Data Mining. Core Data Mining Technologies. Web Database Management. Information Retrieval Systems. Information Management Technologies. The Semantic Web. WEB DATA MINING TECHNIQUES, TOOLS, AND TRENDS. Data Mining and the Web. Processes and Techniques for Web Data Mining. Mining the Databases on the Web. Information Retrieval and Web Data Mining. Information Management and Web Data Mining. Semantic Web Mining. Mining Usage Patterns and Structure on the Web. Prototypes, Products, and Standards for Web Data Mining. Some Applications for Web Mining. WEB DATA MINING APPLICATIONS FOR COUNTER-TERRORISM. Some Information on Terrorism, Security Threats, and Protection Measures. Web Data Mining for Counter-Terrorism. Mining the Web Databases for Counter-Terrorism. Information Retrieval and Web Mining for Counter-Terrorism. Information Management and Web Mining for Counter-Terrorism. Semantic Web Mining for Counter-Terrorism. Web Usage and Structure Mining for Counter-Terrorism. National Security, Privacy, Civil Liberties, and Web Mining. Revisiting Security Threats with Respect to Web Mining. E-Commerce, Business Intelligence, and Counter-Terrorism. Summary and Directions. Appendices.

Read more less

Book Synopsis

Read more less

Book SynopsisRecommender systems are ubiquitous in modern life and are one of the main monetization channels for Internet technology giants. This book helps graduate students, researchers and practitioners to get to grips with this cutting-edge field and build the thorough understanding and practical skills needed to progress in the area. It not only introduces the applications of deep learning and generative AI for recommendation models, but also focuses on the industry architecture of the recommender systems. The authors include a detailed discussion of the implementation solutions used by companies such as YouTube, Alibaba, Airbnb and Netflix, as well as the related machine learning framework including model serving, model training, feature storage and data stream processing.

Read more less

Book SynopsisHybrid Intelligent Systems for Information Retrieval covers three areas along with the introduction to Intelligent IR, i.e., Optimal Information Retrieval Using Evolutionary Approaches, Semantic Search for Web Information Retrieval, and Natural Language Processing for Information Retrieval. Talks about the design, implementation, and performance issues of the hybrid intelligent information retrieval system in one book Gives a clear insight into challenges and issues in designing a hybrid information retrieval system Includes case studies on structured and unstructured data for hybrid intelligent information retrieval Provides research directions for the design and development of intelligent search enginesThis book is aimed primarily at graduates and researchers in the information retrieval domain. Table of Contents1. Introduction. 2. Matching Functions. 3. Information Retrieval Models. 4. Hybrid Swarm Intelligence Approaches for Optimal Information Retrieval. 5. Information Retrieval and Semantic Search. 6. Ontology Creation using Clustering Technique. 7. Natural Language Processing for Information Retrieval. 8. Deep learning (DL) for Information retrieval (IR). 9. Application of Ontology in Domain Specific Information Retrieval: A case study. 10. Applications of Natural Language Processing and Information Retrieval.

Read more less

Book SynopsisIn what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata mattersâhe vividly illustrates why it matters. âPatricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San Josà State University, USAIf data is the language upon which our modern society will be built, then metadata will be its grammar, the construction of its meaning, the building for its content, and the ability to understand what data can be for us all. We are just starting to bring change into the management of the data that connects our experiences.Metadata Matters explains how metadata is the foundation of digital strategy. If digital assets are to be discovered, they want to be found. The path to good metadata design begins with the realization that digital assets need to be identified, organized, and made available for discovery. This book explains how metadata will help ensure that an organization is building the right system for the right users at the right time. Metadata matters and is the best chance for a return on investment on digital assets and is also a line of defense against lost opportunities. It matters to the digital experience of users. It helps organizations ensure that users can identify, discover, and experience their brands in the ways organizations intend. It is a necessary defense, which this book shows how to build.Trade ReviewDigital technology has become our externalized nervous system. Our mental activities are closely linked to the quality and organization of the data we produce and consult on a daily basis. For our work to be effective and well-coordinated, it is necessary that our metadata system be fit for purpose and regularly updated. John Horodyski's book Metadata Matters is an impassioned plea for intelligent metadata management. It is a must read for Chief Information Officers, Chief Data Officers and anyone concerned with sound knowledge management.—Pierre Lévy, PhD, Fellow of the Royal Society of Canada, CEO of INTLEKT Metadata Inc.Metadata guru John manages to pull off the difficult task of writing a book that’s not only much needed and useful but also highly engaging. In lucid prose, using rich examples from our personal and professional lives, John makes a strong case for metadata and its central role for your digital strategy. You’ll learn how metadata can increase the return on investment of marketing and content systems. There’s practical guidance, best practices and more to put all this knowledge into practice as well. A must read for marketing, content, and digital professionals.—Kashyap Kompella, CEO, RPA2AI ResearchMetadata is about making information accessible, and John Horodyski has made the subject of metadata accessible to all in this very readable book that not only teaches principles of metadata but also increases our awareness and appreciation of metadata. Drawing on his rich experience as consultant, Horodyski thoroughly addresses metadata in all fields and industries. This book is not limited to those who plan to manage their metadata but is for anyone who wonders whether they need to or whether they should even care.—Heather Hedden, Author, The Accidental TaxonomistFrom helping us make our everyday choices to making our machines smart, metadata powers our world. John's book is a love song sung to the stuff that's about stuff, full of stories that will entertain you and examples that will help you understand, craft, and choose metadata that indeed matters.—Louis Rosenfeld, co-author of Information Architecture and publisher and founder, Rosenfeld MediaIn what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata matters—he vividly illustrates why it matters. As a Digital Asset Management (DAM) and Metadata Expert, Horodyski is uniquely aware that metadata itself is an asset that is needed to provide context so that other information can be located, retrieved, managed, and interpreted. He distinguishes among different types of metadata—descriptive, administrative, and structural—and discusses the usefulness of metadata standards to provide consistency which can facilitate findability, migration, and interoperability, as well as result in cost savings. Whether new to the concept of metadata or veteran metadata specialists, by the end of the book, all readers will be metadata champions!—Patricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San José State University, USAFinally, an easy-to-read handbook that explores how metadata can inspire us to unlock the potential of the information we create. In Metadata Matters, John Horodyski delivers practical, real-world examples of how putting metadata to work can help us develop differentiating capabilities that would be otherwise difficult or impossible to enjoy. Do yourself a favor. Get this book and devour every chapter. You'll discover how effectively using metadata can dramatically advance the role of content across your enterprise.—Scott Abel, The Content WranglerTable of Contents1 In Praise of Metadata: Lost and Found 2 Metadata: Some Assembly Required 3 Taxonomical Tenets 4 Definitions 5 Adjectivity: Language, Meaning, and Optimization for Content Curation and Discovery 6 Metadata Is a Human Endeavor 7 Governance 8 Metadata and Workflow 9 What Do Good Metadata, UX, and Search Look Like? 10 Please Feed the Robots Good Data 11 Building a Metadata Strategy 12 Metadata Maturity 13 Metadata Is a Love Note to the Future . . . Appendix: Metadata Manifesto Glossary Index

Read more less

Book SynopsisThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results. Trade Review"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia University"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia UniversityTable of Contents1. Introduction. 2. Illustrative Example: Predicting Risk of Ischemic Stroke. 3. A Review of the Predictive Modeling Process. 4. Exploratory Visualizations. 5. Encoding Categorical Predictors. 6. Engineering Numeric Predictors. 7. Detecting Interaction Effects. 8. Handling Missing Data. 9. Working with Profile Data. 10. Feature Selection Overview. 11. Greedy Search Methods. 12. Global Search Methods.

Read more less

Book SynopsisThis book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book's integrated approach to investigating both fields enhances the value of research findings. Table of ContentsIntroduction to Data Analytics (DA). Why Data Analytics in Project Management (PM)? The Importance of DA in PM. The Key role of Data Analytics in Business Analysis. Business Analysis in Managing Projects. Earned Value Method. IT solutions of DA as Applied to PM. How to manage Big Data issues in Projects’ Environment. Data Mining and the Project Management Office. Project Portfolio Management. Future Directions.

Read more less

Book SynopsisThe book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way.At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book.Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout. Trade Review"This clean and fun book covers a wide range of topics on statistical communication, programming, and modeling in a way that should be a useful supplement to any statistics course or self-learning program. I absolutely love this book!"- Andrew Gelman, Columbia University"An excellent book. Communication and reproducibility are of increasing concern in statistics, and this book covers these topics and more in a practical, appealing, and truly unique way."- Daniela Witten, University of Washington"Many data science texts tell you how to perform perfunctory calculations. Instead, Telling Stories with Data tells you how to engage in the mindset and process of analysis. By arming students with the computational, statistical and philosophical skills needed to use data in sense-making and story-telling, this book stands out from the pack as uniquely actionable and empowering."- Emily Riederer, Capital One"This is not another statistics book. It is much better than that. It is a book about doing quantitative research, about scientific justification, about quality control, about communication and epistemic humility. It's a valuable supplement to any methods curriculum, and useful for self-learners as well."- Richard McElreath, Max Planck Institute for Evolutionary Anthropology"Telling Stories with Data is a thoughtful guide to using data to learn and affect positive change. The book includes each stage of the process and can serve as a long-lasting companion to many data scientists and future data story tellers."- Christopher Peters, Zapier“A clever career choice is to pick a field where your skills are complementary with a growing resource. In the coming decades, those who are adept in analysing data will flourish. That means crunching statistics and telling compelling stories. Rohan Alexander’s book will help you do both.”- Andrew Leigh, Member of the Australian Parliament and author of Randomistas: How Radical Researchers Are Changing Our World"Every data analyst has to tell stories with data, and yet traditional textbooks focus on statistical methods alone. Telling Stories with Data teaches the entire data science workflow, including data acquisition, communication, and reproducibility. I highly recommend this unique book!"- Kosuke Imai, Harvard University"This is an extraordinary, wonderful, book, full of wise advice for anyone starting in data science. Intermixing concepts and code means the ideas are immediately made concrete, and the emphasis on reproducible workflows brings a welcome dose of rigor to a rapidly developing field."- David Spiegelhalter, The University of CambridgeTable of Contents1. Telling stories with data 2. Drinking from a fire hose 3. Reproducible workflows Part 1. Foundations 4. Writing research 5. Static communication Part 2. Communication 6. Farm data 7. Gather data 8. Hunt data Part 3. Acquisition 9. Clean and prepare 10. Store and share Part 4. Preparation 11. Exploratory data analysis 12. Linear models 13. Generalized linear models 14. Causality from observational data 15. Multilevel regression with post-stratification 16. Text as data 17. Concluding remarks

Read more less

Book SynopsisHow can we give data physical form?And how might those creations change the ways we experience data and the stories it can tell?Making with Data: Physical Design and Craft in a Data-Driven World provides a snapshot of the diverse practices contemporary creators are using to produce objects, spaces, and experiences imbued with data. Across 25+ beautifully-illustrated chapters, international artists, designers, and scientists each explain the process of creating a specific data-driven pieceâillustrating their practice with candid sketches, photos, and design artifacts from their own studios.The author website, featuring updates and more information about the projects behind the book, can be found here: https://makingwithdata.org/.Featuring influential voices in computer science, data science, graphic design, art, craft, and architecture, Making with Data is accessible and inspiring for entTrade Review"A mind-blowing collection! With the rich visual process descriptions, the creators invite us into their workshops and let us look over their shoulders. You will discover both an exhibition of wonderful data-inspired works as well as the backstories of each of these pieces. Whether hand-made, machine-controlled, or through natural processes, all the chapters show fascinating and bespoke creations of data objects. A much needed collection highlighting what is happening at the frontiers of art and sciences in this new field of data design."-- Giorgia Lupi, partner at Pentagram and author of Dear Data"What a much-needed book! Till, Sam, Lora, and Wes show us that data communication can be so much more than just visualization. There is a whole exciting world of data physicalization waiting to be explored, and the authors open the door for us and lead us through it with intelligent commentary. The book takes us to visit different artists, who explain their approaches and tools – from copper pipes to paper, from wood to electronics. It's a hugely inspiring tour. Reading this book will make you want to experiment with data in the realm of the physical."-- Lisa Charlotte Muth, data vis designer and writer at Datawrapper "This book has fresh inspirations from innovative artist-inventors who open up new possibilities for anyone who has data that tells a story. The screen is no longer the goal or the limit; freeing designers to explore more dimensions and shape deeper experiences to reach people with important messages about their health, communities, and climate. Data physicalizations break free into new dimensions where playful imaginations can use water, plastic, wood, or stone to fabricate data stories for public installations and private reflections. This book makes me want to turn on the laser cutter and restart the 3D printer to fabricate something startling, informative, and eye opening."-- Ben Shneiderman, Professor, Computer science, University of Maryland, USA"A collection of recent and diverse data-driven physical artifacts and sensorial experiences. Projects are beautifully illustrated and described in jargon-free language packed with practical information elucidating the design process, from the tools used to the context of their conception. Making with Data is an invaluable resource for educators and practitioners alike. It broadens our perspective of representing data by engaging all our senses."-- Isabel Meirelles, Professor, Faculty of Design, OCAD University, Toronto, Canada"“Designing with Data” is one of today’s key mantras. What next? Perhaps “Making with Data”, as argued by professors Huron, Nagel, Oehlberg and Willett. This timely book explores new ways data is penetrating our living environment and is crossing the boundary between the physical and the digital. Innovative fabrication methods lend materiality to data, as designers experiment with the use of laser cutters and 3D printers to transform maps and charts into tactile models and artworks. A compelling read for any data enthusiast!"-- Carlo Ratti, Director, MIT Senseable City Lab, USATable of Contents1. Handcraft - Introduction by Sheelagh Carpendale and Lora Oehlberg. 1.1 Snow Water Equivalent by Adrien Segal. 1.2 Life in Clay by Alice Thudt. 1.3 V-Pleat Data Origami by Sarah Hayes. 1.4 Anthropocene Footprints by Mieka West. 1.5 Endings by Loren Madsen. 2. Participation - Introduction by Georgia Panagiotidou and Andrew Vande Moere. 2.1 Cairn by Pauline Gourlet and Thierry Dassé. 2.2 SeeBoat by Laura Perovich. 2.3 Let’s Play with Data by Jose Duarte and EasyDataViz. 2.4 100% [City] by Rimini Protokoll (Helgard Haug, Stefan Kaegi, and Daniel Wetzel). 2.5 Data Strings by Daniel Pearson, Pau Garcia, and Alexandra de Requesens. 3. Digital Production - Introduction by Yvonne Jansen. 3.1 Chemo Singing Bowl by Stephen Barrass. 3.2 Wage Islands by Ekene Ijeoma. 3.3 Data That Feels Gravity by Volker Schweisfurth. 3.4 Orbacles by MINN_LAB Design Collective (Daniel F. Keefe, Ross Altheimer, Andrea J. Johnson, Mahdieh Mahmoudi, Patrick Moe, Maura Rockcastle, Marc Swackhamer, and Aaron Wittkamper). 3.5 Dataseeds by Nick Dulake and Ian Gwilt. 4 Actuation - Introduction by Pierre Dragicevic. 4.1 Tenison Road Charts by David Sweeney, Alex Taylor, and Siân Lindley. 4.2 LOOP by Kim Sauvé and Steven Houben. 4.3 AirFIELD by Nik Hafermaas, Dan Goods, and Jamie Barlow. 4.4 EMERGE by Jason Alexander, Faisal Taher, John Hardy, and John Vidler. 4.5 Zooids by Mathieu Le Goc, Charles Perin, Sean Follmer, Jean-Daniel Fekete, and Pierre Dragicevic. 5. Environment - Introduction by Dietmar Offenhuber. 5.1 Perpetual Plastic by Liina Klauss, Moritz Stefaner and Skye Morét. 5.2 Dataponics: Human-Vegetal Play by Robert Cercós. 5.3 Solar Totems by Charles Sowers. 5.4 Staubmarke (Dustmark) by Dietmar Offenhuber.

Read more less

Book SynopsisThis book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.Table of Contents1. Evolutionary Intelligence. 2. Heart Disease Diagnosis. 3. Diabetes Prediction and Classification. 4. Degenerative Diseases. 5. Tuberculosis. 6. Muscular Dystrophy. 7. Tumor Prediction and Classification.

Read more less

Book SynopsisThe Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With that context, readers will work through practical solutions to resolving problems in data using the statistical and data science programming language R. These solutions include examples of complex real-world data, adding greater context and exposing the reader to greater technical challenges. This book focuses on the Import to Tidy to Transform steps. It demonstrates how âœVisualiseâ is an important part of Exploratory Data Analysis, a strategy for identifying potential problems with the data prior to cleaning.This book is designed for readers with a working knowledge of data manipulation functions in R or other programming languages. It is suitable for academics for whom analyzing data is crucial, businesses who make decisions based on the insights gleaned from collecting d

Read more less

Book SynopsisThis book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learningTable of Contents1. Introduction 2. Overview of Python for Machine Learning 3. Data Analytics Life Cycle for Machine Learning 4. Unsupervised Learning 5. Supervised Learning: Regression 6. Supervised Learning: Classification 7. Feature Engineering 8. Reinforcement Learning 9. Case Studies for Decision Sciences Using Python

Read more less

Book SynopsisUrban Freight Analytics examines the key concepts associated with the development and application of decision support tools for evaluating and implementing city logistics solutions. New analytical methods are required for effectively planning and operating emerging technologies including the Internet of Things (IoT), Information and Communication Technologies (ICT), and Intelligent Transport Systems (ITS).The book provides a comprehensive study of modelling and evaluation approaches to urban freight transport. It includes case studies from Japan, the US, Europe, and Australia that illustrate the experiences of cities that have already implemented city logistics, including analytical methods that address the complex issues associated with adopting advanced technologies such as autonomous vehicles and drones in urban freight transport.Also considered are future directions in urban freight analytics, including hyperconnected city logistics based on the Physical ITable of ContentsPart I. Methods. 1. Introduction. 2. Data collection and analyses. 3. Geographic information systems and spatial analysis. 4. Optimisation. 5. Multi-agent simulation with machine learning. 6. Reliability and resilience. 7. Evaluation. Part II. Applications. 8. Autonomous Vehicles and Robots. 9. Access management and pricing. 10. Environmental sustainability. 11. Disruption of Networks. 12. Future directions.

Read more less

Book SynopsisTaking a hands-on and holistic approach to data, Data + Journalism provides a complete guide to reporting data-driven stories. This book offers insights into data journalism from a global perspective, including datasets and interviews with data journalists from countries around the world. Emphasized by examples drawn from frequently updated sets of open data posted by authoritative sources like the FBI, Eurostat and the US Census Bureau, the authors take a deep dive into data journalism's heavy lifting searching for, scraping and cleaning data. Combined with exercises, video training supplements and lists of tools and resources at the end of each chapter, readers will learn not just how to crunch numbers but also how to put a human face to data, resulting in compelling, story-driven news stories based on solid analysis. Written by two experienced journalists and data journalism teachers, Data + Journalism is essential reading for students, instructorTable of ContentsIntroductionChapter 1: Acquiring DataChapter 2: Searching the Deep WebChapter 3: Scraping DataChapter 4: Cleaning DataChapter 5: Basic SpreadsheetsChapter 6: Advanced Spreadsheets and RChapter 7: Writing a Data StoryChapter 8: SQLChapter 9: Scraping Social MediaChapter 10: Data VisualizationChapter 11: Ethics, Trust, Transparency and Posting Data OnlineChapter 12: Math for Journalists: Writing with Numbers

Read more less

Book SynopsisKnowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. Knowledge Integration Methods provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. The book also provides the mathematical background to solving problems of restoring consistency and integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more. This text will be useful for computer science graduates and PhD students, in addition to researchers and readers working on knowledge management and ontology interpretation.Table of Contents1. Introduction 2. Probabilistic Knowledge-based Systems 3. Consistency Measures for Probabilistic Knowledge Bases 4. Methods for Restoring Consistency in Probabilistic Knowledge Bases 5. Distance-Based Methods for Integrating Probabilistic Knowledge Bases 6. Value-based Method for Integrating Probabilistic Knowledge Bases 7. Experiments and Applications 8. Conclusions and Open Problems

Read more less

Book SynopsisUrban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an ideal onramp for the study of urban informatics and how to translate novel data sets into new insights and practical tools.The book follows a unique pedagogical approach developed by the author to enable students to build skills by pursuing projects that inspire and motivate them. Each chapter has an Exploratory Data Assignment that prompts readers to practice their new skills on a data set of their choice. These assignments guide readers through the process of becoming familiar with the contents of a novel data set and communicating meaningful insights from the data to others.Key Features: The technical curriculum consists of both data management and analytics, including both as needed to become acquainted with and reveal the content of a new data set. Content that is contextualized in real-world applications relevant to community concerns. Unit-level assignments that educators might use as midterms or otherwise. These include Community Experience assignments that prompt students to evaluate the assumptions they have made about their data against real world information. All data sets are publicly available through the Boston Data Portal. Table of Contents1 Introduction 2 Welcome to R 3 Telling a Data Story: Examining Individual Records 4 The Pulse of the City: Observing Variable Patterns 5 Uncovering Information: Making and Creating Variables 6 Measuring with Big Data 7 Making Measures from Records: Aggregating and Merging Data 8 Mapping Communities 9 Advanced Visual Techniques 10 Beyond Measurement: Inferential Statistics (and Correlations) 11 Identifying Inequities across Groups: ANOVA and t-Test 12 Unpacking Mechanisms Driving Inequities: Multivariate Regression 13 Advanced Analytic Techniques 14 Emergent Technologies

Read more less

Book SynopsisPulling aside the curtain of Big Data' buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes. Bad data begets bad decisions, and an understanding of data fundamentals how data is generated, organized, stored, evaluated, and maintained has never been more important when solving problems such as the pandemic-related supply chain crisis. This book addresses the data-related challenges that businesses face, answering questions such as: What are the characteristics of high-quality data? How do you get from bad data to good data? What procedures and practices ensure high-quality data? How do you know whether your data supports the decisions you need to make? This clear and valuable resource will appeal to C-suite executives and top-line managers across industries, as wTable of Contents1 Preface. 2 Data – Introduction. 3 The Many Facets of Data. 3.1 Basic Concepts. 3.2 Basic Terms and Terminology. 4 Domain Specific Topics. 4.1 Data Governance. 4.2 Data Architecture. 4.3 Databases. 4.4 Master Data and Master Data Management. 4.5 Metadata and Metadata Management. 4.6 Data Quality. 4.7 Null Values. 4.8 Data Modeling and Design. 4.9 Data Integration and Interoperability. 4.10 Data Security. 4.11 Data at Rest and Data in Motion. 4.12 Data Wrangling and Data Storage. 5 Data: Past, Present and Future. 5.1 Data – The Past. 5.2 Data – The Present. 5.3 Data – The Future. 6 The New Reality. 7 Data – Use Cases.8 To Sum Up. 9 Data – Optimization. 10 Epilog

Read more less

Book SynopsisRecommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.

Read more less

Book SynopsisDatabases have become an integral part of modern-day life. We live in an information-driven society and database technology has a direct impact on our daily lives. Decisions are routinely made by organizations based on the information collected and stored in the databases. Because databases play such an important role in business and society, database programming is a key skill.SQL Server Database Programming with C#: Desktop and Web Applications is for college students and software programmers who want to develop practical and commercial skills in database programming with C# or Visual C#.NET 2022 as well as the relational database Microsoft SQL Server 2019. The book explains the practical considerations and applications in database programming with Visual C# 2022 and provides realistic examples and detailed explanations. A direct writing style is combined with real-world examples to provide readers with a clear picture of how to handle database programming issues in the VisTable of ContentsCopyrights and Trademarks. Preface. Acknowledgements. About the Author. Chapter 1 Introduction. Chapter 2 Introduction to Databases. Chapter 3 Introduction to ADO.NET. Chapter 4 Introduction to Language Integrated Query (LINQ). Chapter 5 Data Selection Query with Visual C#.NET. Chapter 6 Data Inserting with Visual C#.NET. Chapter 7 Data Updating and Deleting with Visual C#.NET. Chapter 8 Accessing Data in ASP.NET. Chapter 9 ASP.NET Web Services. Index.

Read more less

Book SynopsisWith this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the different types of analysis commonly conducted with big data, this accessible reference presents clear-cut explanations of the general workings of big data tools. Instead of spending time on HOW to install specific packages, it focuses on the reasons WHY readers would install a given package.The book provides authoritative guidance on a range of tools, including open source and proprietary systems. It details the strengths and weaknesses of incorporating big data analysis into decision-making and explains how to leverage the strengths while mitigating the weaknesses. Describes the benefits of distrTable of ContentsIntroduction. The Mother of Invention’s Triplets: Moore’s Law, the Proliferation of Data, and Data Storage Technology. Hadoop. HBase and Other Big Data Databases. Machine Learning. Statistics. Google. Geographic Information Systems. Discovery. Data Quality. Benefits. Concerns.

Read more less

Book SynopsisThis book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader. Upon completion of the text, the reader will be able to: Gain proficiency in R programming Load and manipulate data frames, and tidy them using tidyverse tools Conduct statistical analyses and draw meaningful inferences from them Perform modeling from numerical and textual data Generate data visualizations (numerical and spatialTable of Contents1. Data Types 2. Data Transformation 3. Data Visualization 4. Building Simulations 5. Sampling 6. Hypothesis Testing 7. Quantifying Uncertainty 8. Towards Normality 9. Regression 10. Text Analysis

Read more less

Book SynopsisThere currently is no in-depth book dedicated to the challenge of the Internet of Everything and Big Data technologies in smart cities. Humankind today is confronting a critical worldwide portability challenge and the framework that moves cities must keep pace with the innovation. Internet of Everything and Big Data: Major Challenges in Smart Cities reviews the applications, technologies, standards, and other issues related to smart cities.This book is dedicated to addressing the major challenges in realizing smart cities and sensing platforms in the era of Big Data cities and Internet of Everything. Challenges vary from cost and energy efficiency to availability and service quality. This book examines security issues and challenges, addresses the total information science challenges, covers exploring and creating IoT environment-related sales adaptive systems, and investigates basic and high-level concepts using the latest techniques implemented by researchers Table of Contents1. Wireless Sensor Networks in Smart Cities. 2. Big Data Analytics. 3. Security Issues in Smart Cities. 4. Artificial Intelligence in Smart-Cities. 5. Performability in IoT-enabled Sensors. 6. Data delivery in IoT-enabled Smart Cities. 7. Deployment Issues in IoT-enabled Sensors. 8. Traffic Modelling in Smart-Cities. 9. Resource Management and Enabling Technologies Localization in IoT-enabled Sensors. 10. Modeling and Simulation with Fuzzy Techniques in Smart Cities. 11. Energy Efficiency in Smart Cities Technologies. 12. Semantic Interoperability for IoT.

Read more less

Book SynopsisThe book focuses on a paradigm of blockchain technology that addresses cyber security. The challenges related to cyber security and the solutions based on Software Defined Networks are discussed. The book presents solutions to deal with cyber security attacks by considering real-time applications based on IoT, Wireless Sensor Networks, Cyber-Physical Systems, and Smart Grids. The book is useful for academicians and research scholars worldwide working in cyber security. It is also useful for industry experts working in cyber security.

Read more less

Book SynopsisThe 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earth's conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in theminds of leaders, newknowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing new risks.Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human andTable of Contents1. Fundamental Concepts 2. Risk Management, Modelling, and Analytics Processes 3. Decision Making under Risk and Its Analytics Support 4. Risk Management and Analytics in Organizations 5. Tools for Risk Management 6. Data Analytics in Risk Management 7. Machine and Statistical Learning in Risk Analytics 8. Dealing with Monitoring the Risk Analytics Process 9. Creation of Actions and Value

Read more less

Book SynopsisWith the advent of such advanced technologies as cloud computing, the Internet of Things, the Medical Internet of Things, the Industry Internet of Things and sensor networks as well as the exponential growth in the usage of Internet-based and social media platforms, there are enormous oceans of data. These huge volumes of data can be used for effective decision making and improved performance if analyzed properly. Due to its inherent characteristics, big data is very complex and cannot be handled and processed by traditional database management approaches. There is a need for sophisticated approaches, tools and technologies that can be used to store, manage and analyze these enormous amounts of data to make the best use of them.Big Data Concepts, Technologies, and Applications covers the concepts, technologies, and applications of big data analytics. Presenting the state-of-the-art technologies in use for big data analytics. it provides an in-depth discussiTable of ContentsSection A. Understanding Big Data Chapter 1. Overview of Big Data Chapter 2. Challenges of Big Data Chapter 3. Big Data Analytics Section B. Big Data Technologies Chapter 4. Hadoop Ecosystem Chapter 5. NoSQL Databases Chapter 6. Data Lakes Chapter 7. Deep Learning Chapter 8. Blockchain Section C. Big Data Applications Chapter 9. Big Data for Healthcare Chapter 10. Big Data Analytics for Fraud Detection and Prevention Chapter 11. Big Data Analytics in Social Media Chapter 12. Novel Applications and Research Directions in Big Data Analytics

Read more less

Book SynopsisData-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visuali

Read more less

Book SynopsisWhat if I told you something that could empower our third sector and activists to enhance their capacity? From gathering evidence for funding tenders to campaigning for crucial social issues and much more? It's called open data, yet many in social action remain unaware of it. Primarily shaped by corporate entities, open data seems tailored only for technologists, alienating the third sector. But in reality, it's a powerful tool for social change, bolstering civil society, and creating resilient communities.This book argues a simple point: if open data and the digital aspects that support it aren't accessible to all, then what is the point of it? In an age where technology should be seen as a fundamental human right, it's time to rethink outreach. Deeply rooted in grassroots social activism, this book explores a journey that led to collaborations with governments globally, based on real hands-on work, aiming to democratize open data. Through narrative storytelling, we share insights, best practices, procedures, and community-driven approaches. Regardless of your skill set or organization size, from grassroots workers to third-sector professionals and government officers, join us to reshape the perception of open data, fostering change in neighborhoods.Open Data for Everybody: Using Open Data for Social Good is a love letter to open data's transformative power. To create solutions, understanding the problem is crucial. This book seeks to return control to the real expertsâthose living and working within our communities.Discover more at: www.opendataforeverybody.com

Read more less

Book SynopsisCustomer Relationship Management in the Digital Age charts the concepts, strategies, benefits, and technologies of CRM in an evolving and increasingly digital business landscape.It empowers readers with the skills to use CRM to forge enduring customer connections, optimize experiences, and drive loyalty across diverse industries and markets. Building upon existing literature, this guide offers a holistic approach that bridges theory and practice, making complex CRM concepts accessible to a wide audience. It integrates the latest technological advances, market trends, and customer centric initiatives, providing a comprehensive view of CRM's role in an increasingly customer-driven era. Pedagogical features include case studies, practical strategies and real-world examples, as well as chapter summaries and discussion questions to guide the reader through the key learning points of each chapter.This helpful book enables readers to navigate the complexities of CRM im

Read more less

Book Synopsis

Read more less

Book SynopsisUpdated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.

Read more less

Book SynopsisThis book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.Trade Review'Chirag's extensive experience as a teacher shines through in this textbook, which lives up to its promise to be a 'hands on' introduction to data science. Students have a chance to apply their learning to real-life examples from diverse fields, with hands-on examples that build on basic techniques and utilize tools of data science practice throughout the book. I am particularly pleased to see him weave human issues into his approach, putting principles ahead of particular tools and pointing to ethical challenges at various stages of working with data to help his audience develop an appreciation of ways context and interpretation shape data practices. He exposes students to a more nuanced perspective in which human as well as machine input shapes data science outcomes. It is an awareness that we all will need if we are to use data appropriately to tackle the complex challenges we face today.' Theresa Dirndorfer Anderson'Dr. Shah has written a fabulous introduction to data science for a broad audience. His book offers many learning opportunities, including explanations of core principles, thought-provoking conceptual questions, and hands-on examples and exercises. It will help readers gain proficiency in this important area and quickly start deriving insights from data.' Ryen W. White, Microsoft Research AITable of ContentsPart I. Introduction: 1. Introduction; 2. Data; 3. Techniques; Part II. Tools: 4. UNIX; 5. Python; 6. R; 7. MySQL; Part III. Machine Learning: 8. Machine learning introduction and regression; 9. Supervised learning; 10. Unsupervised learning; Part IV. Applications and Evaluations: 11. Hands-on with solving data problems; 12. Data collection, experimentation and evaluation.

Read more less