Joe Celko's data, measurements, and standards in SQL
Joe Celko has looked deep into the code of SQL programmers and found a consistent and troubling pattern - a frightening lack of consistency between their individual encoding schemes and those of the industries in which they operate. This translates into a series of incompatible databases, each one a...
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Burlington, MA :
Elsevier
2009.
|
| Edición: | 1st edition |
| Colección: | Morgan Kaufmann series in data management systems.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627454106719 |
Tabla de Contenidos:
- Front Cover; Joe Celko's Data, Measurements and Standards in SQL; Copyright Page; Contents; Introduction; PART 1 HISTORY, STANDARDS, AND DESIGNING DATA; CHAPTER 1 Scales and Measurements; 1.1. Measurement Theory; 1.1.1. Range, Granularity and Your Instruments; 1.1.2. Range; 1.1.3. Granularity, Accuracy and Precision; 1.2. Defining a Measurement; 1.3. Tolerance; 1.3.1. Scale Conversion Errors; 1.4. Validation; 1.5. Verification; 1.5.1. Erroneous Values; 1.5.2. Phony Values; 1.5.3. Degree of Trust Versus Risk of Error; CHAPTER 2 Validation; 2.1. Look-Up Tables
- 2.1.1. Auxiliary Tables for Noncomputed Data2.2. Check Digits; 2.2.1. Error Detection Versus Error Correction; 2.2.2. Check Digit Algorithms; 2.3. Declarations, not Functions, not Procedures; 2.4. Patterns and Regular Expressions; 2.4.1. Tricks with Patterns; 2.4.2. Results with NULL Values and Empty Strings; 2.4.3. Like is not Equality; 2.4.4. Avoiding the Like Predicate with a Join; 2.4.5. CASE Expressions and Like Predicates; 2.4.6. Similar to Predicates; 2.4.7. Tricks With Strings; 2.4.8. Regular Expression Web Sites; 2.5. Nondatabase Validation; CHAPTER 3 Data Encoding Schemes
- 3.1. Bad Encoding Schemes3.2. Encoding Scheme Types; 3.2.1. Enumeration Encoding; 3.2.2. Measurement Encoding; 3.2.3. Abbreviation Encoding; 3.2.4. Algorithmic Encoding; 3.2.5. Hierarchical Encoding Schemes; 3.2.6. Vector Encoding; 3.2.7. Concatenation Encoding; 3.3. Atomic Versus Scalar; 3.4. Transition States; 3.4.1. State Transitions; 3.4.2. State Transition DDL; 3.4.3. State Transition Tables; 3.4.4. Automatic State Transition Tables; 3.5. General Guidelines for Designing Encoding Schemes; 3.5.1. Use Existing Encoding Standards; 3.5.2. Allow for Expansion
- 3.5.3. Use Explicit Missing Values to Avoid NULLs3.5.4. Translate Codes for the End User; 3.5.5. One True Look-up Table; 3.6. Keep the Codes in the Database; 3.7. Multiple Character Sets; CHAPTER 4 Scales; 4.1. Bit Flags are not Scales; 4.1.1. BITs; 4.1.2. BITs and Booleans; 4.1.3. BIT Vectors; 4.1.4. Replacing BITs; 4.2. Dimensionless Measurements; 4.3. Types of Scales; 4.3.1. Nominal Scales; 4.3.2. Categorical Scales; 4.3.3. Absolute Scales; 4.3.4. Ordinal Scales; 4.3.5. Rank Scales; 4.3.6. Interval Scales; 4.3.7. Ratio Scales; 4.4. Using Scales; 4.5. Scale Conversion; 4.6. Derived Units
- 4.7. Punctuation and Standard Units4.8. General Guidelines for Using Scales in a Database; CHAPTER 5 Data with Ignorance; 5.1. Get it Right; 5.2. Replace Bad Values with a General Dummy Value; 5.3. Replace Bad Values with a Statistical Dummy Value; 5.4. Replace Bad Values to Complete a Statistical Distribution; 5.5. Replace Bad Values with Statistical Profiling; CHAPTER 6 Keys; 6.1. Uniqueness; 6.2. A Key Cannot Be NULL; 6.3. Invariant Or Universally Controlled Values; 6.4. Surrogate And Physical Locators Keys; 6.4.1. Physical Locators for Performance
- 6.4.2. Physical Locators for Lack of a Proper Key
