Database programming Books

Book SynopsisThis Element tackles the problem of generalization with respect to text-based evidence in the field of literary studies. When working with texts, how can we move, reliably and credibly, from individual observations to more general beliefs about the world? The onset of computational methods has highlighted major shortcomings of traditional approaches to texts when it comes to working with small samples of evidence. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. It exemplifies the way mixed methods can be used in complementary fashion to develop nuanced, evidence-based arguments about complex disciplinary issues in a data-driven research environment.Table of ContentsIntroduction, or What's Wrong with Literary Studies?; Part I. Theory: 1. Probable Cause; Part II. Evidence Eve Kraicer, Nicholas King, Emma Ebowe, Matthew Hunter, Victoria Svaikovsky, and Sunyam Bagga; 2. Machine Learning as a Collaborative Process; 3. Results; Part III. Discussion: 4. Don't Generalize (from Case Studies): The Case for Open Generalization; 5. Don't Generalize (At All): The Case for the Open Mind; Conclusion: On the Mutuality of Method.

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Book SynopsisElena Rakhimov has more than 20 years of experience in software architecture and development in a wide spectrum of enterprise and business environments ranging from nonprofit organizations to Wall Street to her current position with a prominent consulting company. Her determination to stay hands-on notwithstanding, Elena managed to excel in the academic arena, having taught relational database programming at Columbia University's highly esteemed Computer Technology and Applications program. She was educated in database analysis and design at Columbia University and in applied mathematics at Baku State University in Azerbaijan.Table of ContentsPreface xvii Introduction to PL/SQL New Features in Oracle 21c xxiiiChapter 1 PL/SQL Concepts 1 Lab 1.1: PL/SQL Architecture 2 PL/SQL Architecture 2 PL/SQL Block Structure 5 How PL/SQL Gets Executed 9 Lab 1.2: PL/SQL Development Environment 10 Getting Started with SQL Developer 10 Getting Started with SQL*Plus 13 Executing PL/SQL Scripts 14 Lab 1.3: PL/SQL: The Basics 18 DBMS_OUTPUT.PUT_LINE Statement 18 Substitution Variable Feature 21 Summary 25Chapter 2 PL/SQL Language Fundamentals 27 Lab 2.1: PL/SQL Language Components 27 PL/SQL Variables 29 PL/SQL Reserved Words 31 Delimiters 32 Literals in PL/SQL 33 Lab 2.2: Anchored Data Types 33 Lab 2.3: Scope of a Variable, Block, Nested Blocks, and Labels 35 Scope of a Variable 35 Nested Blocks and Labels 36 Summary 38Chapter 3 SQL in PL/SQL 39 Lab 3.1: SQL Statements in PL/SQL 39 Initializing Variables with the SELECT INTO Statement 40 Using DML Statements in a PL/SQL Block 41 Using a Sequence in a PL/SQL Block 43 Lab 3.2: Transaction Control in PL/SQL 44 The COMMIT, ROLLBACK, and SAVEPOINT Statements 44 The SET TRANSACTION Statement 47 Summary 48Chapter 4 Conditional Control: IF Statements 49 Lab 4.1: IF Statements 50 IF-THEN Statements 50 IF-THEN-ELSE Statements 52 Lab 4.2: ELSIF Statements 55 Lab 4.3: Nested IF Statements 59 Logical Operators 61 Summary 62Chapter 5 Conditional Control: CASE Statements 65 Lab 5.1: CASE Statements 65 CASE Statements 66 Searched CASE Statements 68 Lab 5.2: CASE Expressions 74 Lab 5.3: NULLIF and COALESCE Functions 78 NULLIF Function 78 COALESCE Function 80 Summary 82Chapter 6 Iterative Control: Part I 85 Lab 6.1: Simple Loops 86 EXIT Statement 87 EXIT WHEN Statement 91 Lab 6.2: WHILE Loops 92 Using WHILE Loops 92 Terminating the WHILE Loop Prematurely 95 Lab 6.3: Numeric FOR Loops 97 Using the IN Option in the Loop 100 Using the REVERSE Option in the Loop 103 Using Iteration Controls in the Loop 104 Terminating the Numeric FOR Loop Prematurely 108 Summary 109Chapter 7 Iterative Control: Part II 111 Lab 7.1: CONTINUE Statement 111 Using the CONTINUE Statement 112 Using the CONTINUE WHEN Statement 116 Lab 7.2: Nested Loops 119 Using Nested Loops 119 Using Loop Labels 120 Summary 122Chapter 8 Error Handling and Built-in Exceptions 125 Lab 8.1: Handling Errors 125 Lab 8.2: Built-in Exceptions 128 Summary 133Chapter 9 Exceptions 135 Lab 9.1: Exception Scope 135 Lab 9.2: User-Defined Exceptions 139 Lab 9.3: Exception Propagation 143 Re-raising Exceptions 148 Summary 149Chapter 10 Exceptions: Advanced Concepts 151 Lab 10.1: RAISE_APPLICATION_ERROR 151 Lab 10.2: EXCEPTION_INIT Pragma 155 Lab 10.3: SQLCODE and SQLERRM 157 Summary 160Chapter 11 Introduction to Cursors 161 Lab 11.1: Types of Cursors 162 Implicit Cursor 162 Explicit Cursor 164 Lab 11.2: Table-Based and Cursor-Based Records 171 Table-Based Records 172 Cursor-Based Records 174 Lab 11.3: Cursor FOR Loops 175 Lab 11.4: Nested Cursors 177 Summary 179Chapter 12 Advanced Cursors 181 Lab 12.1: Parameterized Cursors 181 Lab 12.2: Cursor Variables and Expressions 186 Cursor Variables 187 Cursor Expressions 193 Lab 12.3: FOR UPDATE Cursors 196 Summary 199Chapter 13 Triggers 201 Lab 13.1: What Triggers Are 201 Database Trigger 202 BEFORE Triggers 205 AFTER Triggers 210 Autonomous Transaction 211 Lab 13.2: Types of Triggers 213 Row and Statement Triggers 213 INSTEAD OF Triggers 215 Summary 219Chapter 14 Mutating Tables and Compound Triggers 221 Lab 14.1: Mutating Tables 221 Lab 14.2: Compound Triggers 223 Summary 228Chapter 15 Collections 229 Lab 15.1: PL/SQL Tables 230 Associative Arrays 231 Nested Tables 233 Collection Methods 236 Lab 15.2: Varrays 240 Lab 15.3: Multidimensional Collections 245 Lab 15.4: Collection Iteration Controls and Qualified Expressions 247 Collection Iteration Controls 247 Qualified Expressions 251 Summary 258Chapter 16 Records 259 Lab 16.1: User-Defined Records 259 User-Defined Records 260 Qualified Expressions with Records 262 Record Compatibility 263 Lab 16.2: Nested Records 265 Lab 16.3: Collections of Records 268 Summary 271Chapter 17 Native Dynamic SQL 273 Lab 17.1: EXECUTE IMMEDIATE Statements 274 EXECUTE IMMEDIATE Statement 275 Lab 17.2: OPEN FOR, FETCH, and CLOSE Statements 283 Summary 287Chapter 18 Bulk SQL 289 Lab 18.1: FORALL Statements 290 FORALL Statements 290 SAVE EXCEPTIONS Option 294 INDICES OF Option 296 VALUES OF Option 297 Lab 18.2: The BULK COLLECT Clause 299 Lab 18.3: Binding Collections in SQL Statements 308 Binding Collections with EXECUTE IMMEDIATE Statements 308 Binding Collections with OPEN FOR, FETCH, and CLOSE Statements 314 Summary 318Chapter 19 Procedures 319 Lab 19.1: Creating Nested Procedures 320 Nested Procedures 320 Parameter Modes 321 Forward Declaration 326 Lab 19.2: Creating Stand-Alone Procedures 327 Summary 330Chapter 20 Functions 331 Lab 20.1: Creating Nested Functions 331 Lab 20.2: Creating Stand-Alone Functions 336 Summary 340Chapter 21 Packages 341 Lab 21.1: Creating Packages 341 Creating a Package Specification 342 Creating a Package Body 343 Lab 21.2: Package Instantiation and Initialization 348 Package Instantiation and Initialization 349 Package State 351 Lab 21.3: SERIALLY_REUSABLE Packages 351 Summary 356Chapter 22 Stored Code Advanced Concepts 357 Lab 22.1: Subprogram Overloading 357 Lab 22.2: Result-Cached Functions 363 Lab 22.3: Invoking PL/SQL Functions from SQL Statements 366 Invoking Functions in SQL Statements 367 Using Pipelined Table Functions 368 Using SQL Macros 370 Summary 375Chapter 23 Object Types in Oracle 379 Lab 23.1: Object Types 380 Creating Object Types 381 Using Object Types with Collections 385 Lab 23.2: Object Type Methods 388 Using Constructor Methods 389 Using Member Methods 392 Using Static Methods 393 Comparing Objects 393 Summary 398Chapter 24 Storing Object Types in Tables 399 Lab 24.1: Storing Object Types in Relational Tables 400 Lab 24.2: Storing Object Types in Object Tables 403 Lab 24.3: Type Evolution 405 Summary 410Chapter 25 Dynamic SQL with the DBMS_SQL Package 411 Lab 25.1: Generating Dynamic SQL with the DBMS_SQL Package 412 Summary 420Appendix A PL/SQL Formatting Guide 421 Case 421 Whitespace 421 Naming Conventions 422 Comments 423Appendix B Student Database Schema 425 Table and Column Descriptions 425 9780138062835 TOC 4/20/2023

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Book SynopsisJohn L. Viescas is an independent database consultant with more than 50 years of experience. He began his career as a systems analyst, designing large database applications for IBM mainframe systems. He spent 6 years at Applied Data Research in Dallas, Texas, where he directed a staff of more than 30 people and was responsible for research, product development, and customer support of database products for IBM mainframe computers. While working at Applied Data Research, John completed a degree in business finance at the University of Texas at Dallas, graduating cum laude. John joined Tandem Computers, Inc., in 1988, where he was responsible for the development and implementation of database marketing programs in Tandem's U.S. Western Sales region. He developed and delivered technical seminars on Tandem's relational database management system, NonStop SQL. John wrote his first book, A Quick Reference Guide to SQL (Microsoft Press, 1989), as a research project to dTable of Contents Part I: Relational Databases and SQL Chapter 1: What Is Relational? Chapter 2: Ensuring Your Database Structure Is Sound Chapter 3: A Concise History of SQL Part II: SQL Basics Chapter 4: Creating a Simple Query Chapter 5: Getting More Than Simple Columns Chapter 6: Filtering Your Data Part III: Working with Multiple Tables Chapter 7: Thinking in Sets Chapter 8: INNER JOINs Chapter 9: OUTER JOINs Chapter 10: UNIONs Chapter 11: Subqueries Part IV: Summarizing and Grouping Data Chapter 12: Simple Totals Chapter 13: Grouping Data Chapter 14: Filtering Grouped Data Part V: Modifying Sets of Data Chapter 15: Updating Sets of Data Chapter 16: Inserting Sets of Data Chapter 17: Deleting Sets of Data PART VI Introduction to Solving Tough Problems Chapter 18 “NOT” And “AND” Problems Chapter 19 Condition Testing Chapter 20 Using Unlinked Data and “Driver” Tables Chapter 21 Performing Complex Calculations on Groups Chapter 22: More SQL Server Complex Problems to Solve APPENDICES Appendix A: SQL Standard Diagrams Appendix B: Schema for the Sample Databases Appendix C: Date and Time Functions Appendix D: Suggested Reading

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Book SynopsisYour database is the foundation of your application. Many programmers assume it's also the bottleneck of productive software development, but with the right techniques, this doesn't have to be true. SQL Antipatterns illustrates some of the most common misconceptions and pitfalls software developers face using relational databases. This book helps you to use a database to produce the most efficient results, and turn sluggish, inflexible code and thankless late-night hours into high-quality, reliable solutions and a job well done. The SQL database programming language is the core of most software in the world, but if you're like most software developers, you're self-taught in SQL, and you know just enough to get by. As a result, you might design fragile databases that cannot scale as the business grows, write queries that produce incorrect results and not know why, or deploy applications that are vulnerable to hackers. Don't reinvent the wheel to solve problems SQL was designed to solve--this book will teach you new SQL techniques for getting your job done. Whatever platform or programming language you use, whether you're a junior programmer or a Ph.D., SQL Antipatterns will show you how to design and build databases, how to write better database queries, and how to integrate SQL programming with your application like an expert. You'll also learn the best and most current technology for full-text search, how to design code that is resistant to SQL injection attacks, and other techniques for success.

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Book SynopsisTrade Review"Anyone tasked with business intelligence and data integration will want this outstanding introduction and guidebook…An established professional will also not be disappointed with the fresh how-to look. I highly recommend it." --Computing Reviews "The world of architecture and technology is rapidly changing and has been for a while. Fortunately we have people like Rick Sherman who stay abreast of the latest advances. This book is as close to a complete A to Z guidebook as you will find. I recommend this book for every professional's desk." --Bill Inmon, Forest Rim Technology "Rick covers a lot of ground in the BI Guidebook offering clear explanations of a wide range of BI and DW topics (e.g., technologies, methodologies). His approach makes it both a useful primer for those new to Business Intelligence as well as a great reference for more seasoned pros." --Howard Dresner, Chief Research Officer Dresner Advisory Services "Anyone looking to rollout a business intelligence initiative or shore up a fledgling one can benefit from the BI Guidebook. Rick Sherman leaves no stone unturned and takes a deep dive into a complex and diverse field. Even a seasoned BI professional will enjoy having this book as a reference within arm's reach." --William McNight "One cannot manage a business or run any organization without measurement: knowing where you'v been, where you are and where you are going. But while BI is the key enabler for such quantitative enterprise management, it remains one of the more complicated areas of information technology. BI encompasses a wide range of expertise from the technical mastery of building data models to the psychological finesse of resolving conflicts between IT and business people. This is why educating the market on best practices is so important, and why Forrester encourages reading and studying books that arm people with the knowledge they need to tackle the myriad challenges of implementing BI projects. This book, particularly, confirms Forrester’s position that data is a vital tool and a corporate asset that should have direct impact on enterprise top and bottom lines, as well as balance sheets." --Boris Evelson, Vice President, Principal Analyst, Forrester Research, Inc.Table of ContentsConcepts and Context 1 Introduction Business and Technical Needs 2 Justifying BI (Building Business and Technical Case 3 Defining Requirements - Business, Data and Quality Architectural Framework 4 Architecture Introduction 5 Information Architecture 6 Data Architecture 7 Technology and Product Architectures Data Design 8 Foundational Data Modeling 9 Dimensional Modeling 10 Advanced Dimensional Modeling Data Integration Design 11 Data Integration Processes 12 Data Integration Design & Development BI Design 13 BI Applications 14 BI Design & Development 15 Advanced Analytics 16 Data Shadow Systems Organization 17 People, Process and Politics 18 Project Management 19 Centers of Excellence

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Book SynopsisTrade Review"...well written; is technical as needed, without the pitfall of discussing too-specific implementation options; and has the necessary procedural and governance counterweights to kickstart and sustain multi-domain MDM initiatives…a good reference manual for those who plan to roll out MDM initiatives or have to navigate existing ones." --Computing ReviewsTable of Contents1. Strategy, Scope, and Approach2. Defining and Prioritizing Master Data3. Architecture and Technology Considerations4. Program Management Office Implementation5. Defining a Cross-Domain Maturity Model6. Establishing Multi-Domain Data Governance7. Data Stewardship8. Data Integration9. Data Quality Management10. Metadata Management11. Performance Measurement12. Continuous Improvement

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Book SynopsisUsing real-world examples, this practical guide shows you how to build a simple database application with SQLAlchemy, and how to connect to multiple databases simultaneously with the same metadata.

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Book SynopsisGetting Started with Natural Language Processing is a hands-on guide filled with everything you need to get started with NLP in a friendly, understandable tutorial. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. By following the numerous Python-based examples and real-world case studies, you’ll apply NLP to search applications, extracting meaning from text, sentiment analysis, user profiling, and more. When you’re done, you’ll have a solid grounding in NLP that will serve as a foundation for further learning. Key Features · Extracting information from raw text · Named entity recognition · Automating summarization of key facts · Topic labeling For beginners to NLP with basic Python skills. About the technology Natural Language Processing is a set of data science techniques that enable machines to make sense of human text and speech. Advances in machine learning and deep learning have made NLP more efficient and reliable than ever, leading to a huge number of new tools and resources. From improving search applications to sentiment analysis, the possible applications of NLP are vast and growing. Ekaterina Kochmar is an Affiliated Lecturer and a Senior Research Associate at the Natural Language and Information Processing group of the Department of Computer Science and Technology, University of Cambridge. She holds an MA degree in Computational Linguistics, an MPhil in Advanced Computer Science, and a PhD in Natural Language Processing.

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Book SynopsisPrepare for Microsoft Exam 70-761–and help demonstrate your real-world mastery of SQL Server 2016 Transact-SQL data management, queries, and database programming. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical-thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: • Filter, sort, join, aggregate, and modify data • Use subqueries, table expressions, grouping sets, and pivoting • Query temporal and non-relational data, and output XML or JSON • Create views, user-defined functions, and stored procedures • Implement error handling, transactions, data types, and nulls This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you have experience working with SQL Server as a database administrator, system engineer, or developer • Includes downloadable sample database and code for SQL Server 2016 SP1 (or later) and Azure SQL Database Querying Data with Transact-SQL About the Exam Exam 70-761 focuses on the skills and knowledge necessary to manage and query data and to program databases with Transact-SQL in SQL Server 2016. About Microsoft Certification Passing this exam earns you credit toward a Microsoft Certified Solutions Associate (MCSA) certification that demonstrates your mastery of essential skills for building and implementing on-premises and cloud-based databases across organizations. Exam 70-762 (Developing SQL Databases) is also required for MCSA: SQL 2016 Database Development certification. See full details at: microsoft.com/learningTable of ContentsCHAPTER 1 Manage data with Transact-SQL CHAPTER 2 Query data with advanced Transact-SQL components CHAPTER 3 Program databases by using Transact-SQL

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Book SynopsisBen Forta is Adobe's Senior Director of Education Initiatives and has three decades of experience in the computer industry in product development, support, training, and product marketing. He is the author of the best-selling Sams Teach Yourself SQL in 10 Minutes (including spinoff titles on MariaDB, MySQL, SQL Server T-SQL, and Oracle PL/SQL), Learning Regular Expressions, as well as books on Java, Windows, and more. He has extensive experience in database design and development, has implemented databases for several highly successful commercial software programs and websites, and is a frequent lecturer and columnist on application development and Internet technologies. Ben lives in Oak Park, Michigan, with his wife, Marcy, and their children. He welcomes your email at ben@forta.com and invites you to visit his website at http://forta.com.     Table of ContentsIntroduction 1 Who Is the Teach Yourself SQL Book For? . . . . . . . . . . . . . . . . . . . . . . . . . . 1 DBMSs Covered in This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Conventions Used in This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Understanding SQL 5 Database Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 What Is SQL? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Try It Yourself . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Retrieving Data 13 The SELECT Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Retrieving Individual Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Retrieving Multiple Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Retrieving All Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Retrieving Distinct Rows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Limiting Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Using Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Sorting Retrieved Data 25 Sorting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Sorting by Multiple Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Sorting by Column Position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Specifying Sort Direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Filtering Data 33 Using the WHERE Clause . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 The WHERE Clause Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Advanced Data Filtering 41 Combining WHERE Clauses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Using the IN Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Using the NOT Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496 Using Wildcard Filtering 51 Using the LIKE Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Tips for Using Wildcards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Creating Calculated Fields 59 Understanding Calculated Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Concatenating Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Performing Mathematical Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688 Using Data Manipulation Functions 69 Understanding Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Using Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789 Summarizing Data 79 Using Aggregate Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Aggregates on Distinct Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Combining Aggregate Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8810 Grouping Data 89 Understanding Data Grouping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Creating Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Filtering Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Grouping and Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 SELECT Clause Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9711 Working with Subqueries 99 Understanding Subqueries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Filtering by Subquery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Using Subqueries as Calculated Fields . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10612 Joining Tables 107 Understanding Joins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Creating a Join . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11613 Creating Advanced Joins 117 Using Table Aliases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Using Different Join Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Using Joins with Aggregate Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Using Joins and Join Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12614 Combining Queries 127 Understanding Combined Queries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Creating Combined Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13415 Inserting Data 135 Understanding Data Insertion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Copying from One Table to Another . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14316 Updating and Deleting Data 145 Updating Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Deleting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Guidelines for Updating and Deleting Data . . . . . . . . . . . . . . . . . . . . . . . 149 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15017 Creating and Manipulating Tables 151 Creating Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Updating Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Deleting Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Renaming Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15918 Using Views 161 Understanding Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Creating Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17019 Working with Stored Procedures 171 Understanding Stored Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Understanding Why to Use Stored Procedures . . . . . . . . . . . . . . . . . . . . . 172 Executing Stored Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Creating Stored Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17820 Managing Transaction Processing 179 Understanding Transaction Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Controlling Transactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18521 Using Cursors 187 Understanding Cursors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Working with Cursors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19222 Understanding Advanced SQL Features 193 Understanding Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Understanding Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 Understanding Triggers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Database Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202A Sample Table Scripts 203B SQL Statement Syntax 209C Using SQL Datatypes 215D SQL Reserved Words 221TOC, 9780135182796, 10/9/19

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Book SynopsisThis practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL.

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Book SynopsisTo help you on the path to becoming a Snowflake pro, this concise yet comprehensive guide reviews fundamentals and best practices for Snowflake's SQL and Scripting languages.

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Book SynopsisIntroduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- Appendix A Essential Mathematics.- Appendix B Datasets.- Appendix C Sources of Further Information.- Appendix D Glossary and Notation.- Appendix E SolutioTable of ContentsIntroduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- Appendix A – Essential Mathematics.- Appendix B – Datasets.- Appendix C – Sources of Further Information.- Appendix D – Glossary and Notation.- Appendix E – Solutions to Self-assessment Exercises.- Index.

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Book SynopsisBusinesses are gathering data today at exponential rates and yet few people know how to access it meaningfully. If you're a business or IT professional, this short hands-on guide teaches you how to pull and transform data with SQL in significant ways.

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Book SynopsisA practical cookbook, filled with advanced recipes , and plenty of code and commands used for illustration,which will make your learning curve easy and quick. This book is for anyone who wants to learn more about databases in general or MariaDB in particular. Some familiarity with SQL databases is assumed, but the recipes are approachable to almost anyone with basic database skills.

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Book SynopsisThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.Table of Contents1. Introduction.- Part I: Star Schema.- 2. Simple Star Schemas.- 3. Creating Facts and Dimensions: More Complex Processes.- Part II: Snowflake and Bridge Tables.- 4. Hierarchies.- 5. Bridge Tables.- 6. Temporal Data Warehousing.- Part III: Advanced Dimension.- 7. Determinant Dimensions.- 8. Junk Dimensions.- 9. Dimension Keys.- 10. One-Attribute Dimensions.- Part IV: Multi-Fact and Multi-Input.- 11. Multi-Fact Star Schemas.- 12. Slicing a Fact.- 13. Multi-Input Operational Databases.- Part V: Data Warehousing Granularity and Evolution.- 14. Data Warehousing Granularity and Levels of Aggregation.- 15. Designing Lowest-Level Star Schemas.- 16. Levels of Aggregation: Adding and Removing Dimensions.- 17. Levels of Aggregation and Bridge Tables.- 18. Active Data Warehousing.- Part VI: OLAP, Business Intelligence, and Data Analytics.- 19. Online Analytical Processing (OLAP).- 20. Pre- and Post-Data Warehousing.- 21. Data Analytics for Data Warehousing.

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Book SynopsisBlockchain secures individual transactions with cryptography, creating a tamper-proof chain. In contrast, distributed ledger technologies cover various technologies that may not adhere strictly to blockchain''s specific structure. We will start the book by defining blockchain elements, comparing it to DLT, exploring security mechanisms, and looking at Bitcoin''s history, transactions, wallets, keys, mining, and forensics. Further in the book, we will explore Ethereum''s evolution, Ethereum virtual machine, and smart contracts. We will also discover permissioned blockchains with Hyperledger Fabric and understand crypto assets, ERC20, ERC721 tokens, NFTs, and cryptocurrencies. With real-world examples and practical insights, you will gain a solid grasp of the fundamentals and practical applications that make blockchain a game-changer in various industries.

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Book SynopsisLearn to automate SQL Server operations using frameworks built from metadata-driven stored procedures and SQL Server Integration Services (SSIS). Bring all the power of Transact-SQL (T-SQL) and Microsoft .NET to bear on your repetitive data, data integration, and ETL processes. Do this for no added cost over what you''ve already spent on licensing SQL Server. The tools and methods from this book may be applied to on-premises and Azure SQL Server instances. The SSIS framework from this book works in Azure Data Factory (ADF) and provides DevOps personnel the ability to execute child packages outside a project-functionality not natively available in SSIS.Frameworks not only reduce the time required to deliver enterprise functionality, but can also accelerate troubleshooting and problem resolution. You''ll learn in this book how frameworks also improve code quality by using metadata to drive processes. Much of the work performed by data professionals can be classiTable of ContentsPart I: Stored Procedure-Based Database Frameworks1. Stored Procedures 1012. Automation with Stored Procedures3. Stored Procedure Orchestrators4. A Stored Procedure-Base Metadata-Driven FrameworkPart II: SSIS Frameworks5. A Simple Custom File-Based SSIS Framework6. Framework Execution Engine7. Framework Logging8. Azure-SSIS Integration Runtime9. Deploy A Simple Custom File-Based Azure-SSIS Framework10. Framework Logging in ADF11. Fault Tolerance in the ADF Framework

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Book SynopsisWith an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You’ll get familiar with Python’s functional built-ins like the functools operator and itertools modules, as well as the toolz library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you’ll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level!Key features• An introduction to functional and parallel programming • Data science workflow • Profiling code for better performance • Fulfilling different quality objectives for a single unifying task • Python multiprocessing • Practical exercises including full-scale distributed applicationsAudienceReaders should have intermediate Python programming skills.About the technologyPython is a data scientist’s dream-come-true, thanks to readily available libraries that support tasks like data analysis, machine learning, visualization, and numerical computing. J.T. Wolohan is a lead data scientist at Booz Allen Hamilton and a PhD researcher at Indiana University, Bloomington, affiliated with the Department of Information and Library Science and the School of Informatics and Computing. His professional work focuses on rapid prototyping and scalable AI. His research focuses on computational analysis of social uses of language online.

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Book Synopsis 　 DESCRIPTION Geospatial data is hard to ignore. Nearly every car, phone, or camera has a GPS sensor, and aerial photos, satellite imagery, and data representing political boundaries, roads, rivers, and streams are available for free download from many websites. Geoprocessing is the science of reading, analyzing, and presenting geospatial data programmatically. The Python language, along with dozens of open source libraries and tools, makes it possible to take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo. Geoprocessing with Python teaches how to use the Python programming language along with free and open source tools to read, write, and process geospatial data. It shows how to access available data sets to make maps or perform analyses using free and open source tools like the GDAL, Shapely, and Fiona Python modules. Readers will master core practices like handling multiple vector file formats, editing and manipulating geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. They’ll also learn how to create geospatial data, rather than just consuming it. The book also covers how to manipulate and analyze raster data, such as aerial photographs, satellite images, and digital elevation models. KEY SELLING POINTS Geoprocessing from the ground up Shows how to create custom geoprocessing tools Takes advantage of free and open source tools AUDIENCE Readers need a basic knowledge of Python or a similar programming language. No previous experience with geoprocessing or geospatial data required. Perfect for readers who have used Python with Esri ArcGIS and want to apply their Python skills elsewhere. ABOUT THE TECHNOLOGY Geoprocessing is the science of reading, analyzing, and presenting geospatial data programmatically. The Python language, along with dozens of open source libraries and tools, makes it possible to take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo.

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Book SynopsisWhen machine learning engineers work with data sets, they may find the results aren''t as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data''s features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist''s or machine learning engineer''s toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.Trade Review'Pablo Duboue is a true grandmaster of the art and science of feature engineering. His foundational contributions to the creation of IBM Watson were a critical component of its success. Now readers can benefit from his expertise. His book provides deep insights into to how to develop, assess, combine, and enhance machine learning features. Of particular interest to advanced practitioners is his discussion of feature engineering and deep learning; there is a pervasive myth in the industry that deep learning and big data have made feature engineering obsolete, but the book explains why that is often incorrect for real-world computing applications and explains the relationship between building effective features and deep neural network architectures. The book engages with countless other basic and advanced topics in the area of machine learning and feature engineering, making it a valuable resource for machine learning practitioners of all levels of experience.' J. William Murdock, IBM'Feature engineering is the process of identifying, selecting and evaluating input variables to statistical and machine learning models for a given problem. Pablo Duboue's The Art of Feature Engineering introduces the process with rich detail from a practitioner's point of view, and adds new insights through four input data scenarios for the same prediction task. Highly recommended!' Nelson Correa, Andinum Inc.'TAoFE is a comprehensive handbook - sure to be a hit with data science practitioners. With highly accessible and didactic explanations of complex concepts, the book represents the state-of-the-art, and shows in practical terms how it applies to a wide range of real-world case studies.' Gavin Brown, University of Manchester'This book provides a large catalogue of feature manipulation techniques along with non-trivial examples to illustrate their applicability and impact on performance. It could be suitable as a textbook for an upper level undergrad or graduate text mining or multimodal data analysis class. Recent graduates starting in field data mining and text analysis will find this a useful text.' Wlodek Zadrozny, University of North CarolinaTable of ContentsPart I. Fundamentals: 1. Introduction; 2. Features, combined; 3. Features, expanded; 4. Features, reduced; 5. Advanced topics; Part II. Case Studies: 6. Graph data; 7. Timestamped data; 8. Textual data; 9. Image data; 10. Other domains.

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Book SynopsisMaster a graph data modelling technique superior to traditional data modelling for both relational and NoSQL databases (graph, document, key-value, and column), leveraging cognitive psychology to improve big data designs. From Karen Lopezs Foreword: In this book, Thomas Frisendal raises important questions about the continued usefulness of traditional data modelling notations and approaches: Are Entity Relationship Diagrams (ERDs) relevant to analytical data requirements? Are ERDs relevant in the new world of Big Data? Are ERDs still the best way to work with business users to understand their needs? Are Logical and Physical Data Models too closely coupled? Are we correct in using the same notations for communicating with business users and developers? Should we refine our existing notations and tools to meet these new needs, or should we start again from a blank page? What new notations and approaches will we need? How will we use those to build enterprise database systems? Frisendal takes us through the history of data modelling, enterprise data models and traditional modelling methods. He points out, quite contentiously, where he feels we have gone wrong and in a few places where we got it right. He then maps out the psychology of meaning and context, while identifying important issues about where data modelling may or may not fit in business modelling. The main subject of this work is a proposal for a new exploration-driven modelling approach and new modelling notations for business concept models, business solutions models, and physical data models with examples on how to leverage those for implementing into any target database or datastore. These new notations are based on a property graph approach to modelling data. From the authors introduction: This book proposes a new approach to data modelling -- one that turns the inside out. For well over thirty years, relational modelling and normalisation was the name of the game. One can ask that if normalisation was the answer, what was the problem? There is something upside-down in that approach, as we will see in this book. Data analysis (modelling) is much like exploration. Almost literally. The data modeler wanders around searching for structure and content. It requires perception and cognitive skills, supported by intuition (a psychological phenomenon), that together determine how well the landscape of business semantics is mapped. Mapping is what we do; we explore the unknowns, draw the maps and post the Here be Dragons warnings. Of course there are technical skills involved, and surprisingly, the most important ones come from psychology and visualisation (again perception and cognition) rather than pure mathematical ability. Two compelling events make a paradigm shift in data modelling possible, and also necessary: 1.The advances in applied cognitive psychology address the needs for proper contextual framework and for better communication, also in data modelling, and 2. The rapid intake of non-relational technologies (Big Data and NoSQL).

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Book SynopsisThis Visual Basic book has been a favourite of developers ever since the 1st edition came out in 2002. Now, this 5th Edition is a self-paced, professional book that shows how to use Visual Studio 2012, VB 2012, and the .NET 4.5 classes to develop Windows Forms applications. It is a data programming book that shows how to (1) prototype professional database applications using RAD features like data sources; (2) start using ADO.NET to work directly with databases for more control; and (3) use LINQ to query a variety of data sources, from databases to arrays to XML files. It is an object-oriented book that shows how to use business classes, inheritance, and interfaces the way they are used in the real world. It is a hands-on book that shows you how to develop your first Windows 8 apps and submit them to the Windows Store. When you are done, you will be able to develop 3-tiered, object-oriented Windows applications the way the best professionals do. And you will have the core VB and Visual Studio skills that you need to develop any VB application whether for Windows, the web, or mobile devices.

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Book SynopsisIf you are developing applications that access Oracle databases, you can save time and trouble by having the database do more work for you. That is where this book comes in. It teaches you how to create effective SQL queries to retrieve and update the data in an Oracle database. It teaches you how to design and implement a database, giving you insight into performance and security issues. It teaches you how to use Oracle''s procedural language, PL/SQL, to take advantage of powerful features like stored procedures, functions, and triggers. In short, it teaches you to write the kind of SQL and PL/SQL that makes you a more effective and valuable developer.

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Book Synopsis

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Book SynopsisThis open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.Table of ContentsMultivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder.- Dual Sequential Variational Autoencoders for Fraud Detection.- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks.- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams.- GraphMDL: Graph Pattern Selection Based on Minimum Description Length.- Towards Content Sensitivity Analysis.- Gibbs Sampling Subjectively Interesting Tiles.- Even Faster Exact k-Means Clustering.- Ising-Based Consensus Clustering on Special Purpose Hardware.- Transfer Learning by Learning Projections from Target to Source.- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs.- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces.- Vouw: Geometric Pattern Mining Using the MDL Principle.- A Consensus Approach to Improve NMF Document Clustering.- Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams.- Widening for MDL-Based Retail Signature Discovery.- Addressing the Resolution Limit and the Field of View Limit in Community Mining.- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics.- Adversarial Attacks Hidden in Plain Sight.- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code.- Overlapping Hierarchical Clustering (OHC).- Digital Footprints of International Migration on Twitter.- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks.- A Late-Fusion Approach to Community Detection in Attributed Networks.- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction.- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization.- Actionable Subgroup Discovery and Urban Farm Optimization.- AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model.- Detection of Derivative Discontinuities in Observational Data.- Improving Prediction with Causal Probabilistic Variables.- DO-U-Net for Segmentation and Counting.- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media.- Event Recognition Based on Classification of Generated Image Captions.- Human-to-AI Coach: Improving Human Inputs to AI Systems.- Aleatoric and Epistemic Uncertainty with Random Forests.- Master your Metrics with Calibration.- Supervised Phrase-Boundary Embeddings.- Predicting Remaining Useful Life with Similarity-Based Priors.- Orometric Methods in Bounded Metric Data.- Interpretable Neuron Structuring with Graph Spectral Regularization.- Comparing the Preservation of Network Properties by Graph Embeddings.- Making Learners (More) Monotone.- Combining Machine Learning and Simulation to a Hybrid Modelling Approach.- LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.- Angle-Based Crowding Degree Estimation for Many-Objective Optimization.

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Book SynopsisFind tips for creating efficient PL/SQL code If you know a bit about SQL, this book will make PL/SQL programming painless! The Oracle has spokenyou need to get up to speed on PL/SQL programming, right? We predict it''ll be a breeze with this book! You''ll find out about code structures, best practices, and code naming standards, how to use conditions and loops, where to place PL/SQL code in system projects, ways to manipulate data, and more. Discover how to Write efficient, easy-to-maintain code Test and debug PL/SQL routines Integrate SQL and PL/SQL Apply PL/SQL best practices Use new features introduced in Oracle 9i and 10g Table of ContentsIntroduction. Part I: Basic PL/SQL Concepts. Chapter 1: PL/SQL and Your Database. Chapter 2: The PL/SQL Environment. Part II: Getting Started with PL/SQL. Chapter 3: Laying the Groundwork: PL/SQL Fundamentals. Chapter 4: Controlling Program Flow. Chapter 5: Handling Exceptions. Chapter 6: PL/SQL and SQL Working Together. Part III: Standards and Structures. Chapter 7: Putting Your Code in the Right Place. Chapter 8: Creating Naming Standards. Chapter 9: Creating Coding Standards. Part IV: PL/SQL Data Manipulations. Chapter 10: Basic Datatypes. Chapter 11: Advanced Datatypes. Part V: Taking PL/SQL to the Next Level. Chapter 12: Transaction Control. Chapter 13: Dynamic SQL and PL/SQL. Chapter 14: PL/SQL Best Practices. Part VI: The Part of Tens. Chapter 15: Ten PL/SQL Tips. Chapter 16: Ten Common Mistakes to Avoid in PL/SQL. Index.

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Book SynopsisThis handy resource provides scores of short, focused pieces of code, hundreds of worked-out examples, and clear, concise explanations for programmers who don't have the time (or expertise) to resolve MySQL problems from scratch. This updated fourth edition provides more than 200 recipes that cover powerful features in both MySQL 5.7 and 8.0.

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Book SynopsisIm einführenden Kapitel dieses Datenmanagementbuchs geht es zunächst um die Frage, wie im Zuge der Digitalisierung die Umwandlung von analogen Daten in binäre Zahlen abläuft und welche Auswirkungen dies auf die computergesteuerte Verarbeitung von Texten, Bildern, Farben sowie Musik hat. Des Weiteren betrachten die Verfasser folgende Gesichtspunkte: Grundlagen von DatenbankenDatenbankentwürfeMalwareMögliche DatenschutzmaßnahmenTable of ContentsDigitale Daten - Dateien - Datenbanken - Datenschutz – Datensicherheit

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