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Advanced Standard SQL Dynamic Structured Data Modeling and Hierarchical Processing.

By: Contributor(s): Material type: TextTextPublisher: Norwood : Artech House, 2013Copyright date: ©2013Edition: 1st edDescription: 1 online resource (406 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781608075348
Subject(s): Genre/Form: Additional physical formats: Print version:: Advanced Standard SQL Dynamic Structured Data Modeling and Hierarchical ProcessingDDC classification:
  • 005.7565
LOC classification:
  • QA76.73.S67 -- D392 2013eb
Online resources:
Contents:
Intro -- Advanced Standard SQL Dynamic Structured Data Modeling and Hierarchical Processing -- Contents -- Preface -- Introduction -- Part I: The Basics of the RelationalJoin Operation -- 1 Relational Join Introduction -- 1.1 Standard Inner Join Review -- 1.2 Problems with Relational Join Processing -- 1.3 Outer Join Review -- 1.4 Problems with Previous Outer Join Syntax -- 1.5 Conclusion -- 2 The Standard SQL Join Operation -- 2.1 Standard SQL Join Syntax -- 2.2 Standard SQL Join Operation -- 2.3 Standard SQL Join Does Not Follow the Cartesian Product Model -- 2.4 Determining Standard SQL Join Associativity and Commutativity -- 2.5 What Outer Join Commutativity Is -- 2.6 What Outer Join Associativity Is -- 2.7 Hierarchictivity in Addition to Associativity and Commutativity -- 2.8 Conclusion -- 3 Standard SQL Join Types and Their Operation -- 3.1 FULL Outer Join -- 3.2 One-Sided Outer Join -- 3.3 INNER Join -- 3.4 CROSS Join -- 3.5 UNION Join -- 3.6 Intermixing Join Types -- 3.7 Conclusion -- 4 Natural Joins -- 4.1 Explicit and Implicit Natural Joins -- 4.2 Multitable Natural Outer Joins -- 4.3 Natural One-Sided Outer Join -- 4.4 Natural FULL Outer Join -- 4.5 Natural Inner Joins -- 4.6 Intermixing Natural Join Types -- 4.7 Natural One-Sided Join Transformation -- 4.8 Conclusion -- Part II: Outer Join Data Modeling and Structured Processing -- 5 Data Structure Review -- 5.1 The Power of Hierarchical Data Structures -- 5.2 Three-Tier Database Architecture -- 5.3 External and Internal Views -- 5.4 Conceptual View -- 5.5 Many-to-One and One-to-Many Relationships -- 5.6 Many-to-Many Relationships -- 5.7 Converting Network Structures to Hierarchical Structures -- 5.8 Relating Hierarchical Processing to Relational Processing -- 5.9 Physical Versus Logical Data Structures -- 5.10 Sibling Legs Query Semantics.
5.11 Ordering of Data Structures Can Cause Their Restructuring -- 5.12 Data Structure Composition -- 5.13 Good Data Modeling Design Principles -- 5.14 Conclusion -- 6 Outer Join Does Data Modeling -- 6.1 SQL Data Modeling Using the Outer Join -- 6.2 ON Clause Data Modeling Join Condition Rules -- 6.3 Valid and Invalid ON Clause Data Modeling Examples -- 6.4 Valid and Invalid Data Modeling Results -- 6.5 Substructure Views -- 6.6 WHERE Clause Filtering with Data Structures -- 6.7 WHERE Clause Filtering with Substructures -- 6.8 Complex Data Modeling Example -- 6.9 Conclusion -- 7 Outer Join Data Modeling-Related Capabilities -- 7.1 Data Structure Filtering -- 7.2 Indirect Structure Linking -- 7.3 Nonhierarchical Join Type Support -- 7.4 Nonhierarchical Joining of Data Structures -- 7.5 Many-to-Many Data Modeling and Intersecting Data -- 7.6 Conclusion -- 8 More About Outer Join Data Modeling -- 8.1 Importance of SQL's Inherent Data Structure Processing Ability -- 8.2 Efficient Client/Server Data Structure Processing -- 8.3 Coding Data Modeling Outer Join Statements -- 8.4 Generation of Data Modeling Outer Join Statements -- 8.5 Hierarchical Data Structure Processing Empirical Proof -- 8.5.1 Hierarchical Control -- 8.5.2 Structure Control -- 8.6 Nonhierarchical Data Structure Processing Empirical Proo -- 8.7 Embedded Structured View Support Empirical Proof -- 8.8 Indirect Link Empirical Proof -- 8.9 SQL:1999 and Data Modeling -- 8.10 What Makes the ANSI Standard Outer Join Unique for Data Modeling -- 8.11 Data Modeling with Old-Style Outer Joins -- 8.12 The New Role of the Inner Join Operation -- 8.13 Conclusion -- Part III: New Capabilities Based on Outer JoinData Modeling -- 9 Data Structure Extraction (DSE) Technology -- 9.1 Extracting Data Structure Information From the Outer Join -- 9.2 DSE Example -- 9.3 Logical Table Example.
9.4 Symmetric Linking of Data Structures Example -- 9.5 DSE Internal Logic -- 9.6 Why Vendors Need the DSE Technology -- 9.7 DSE Avoids Imposing Data Structures on SQL -- 9.8 Conclusion -- 10 Outer Join Advanced Capabilities -- 10.1 Database Navigation -- 10.2 Access Optimizations -- 10.3 Enterprise and Legacy Database Access -- 10.4 Open Database Access Interface -- 10.5 Seamless Value-Added Features -- 10.6 Data Warehouse Interface -- 10.7 Hierarchical Relational Processing -- 10.8 Object Relational Interface -- 10.9 View Update Capability -- 10.10 Multimedia Application Directory Support -- 10.11 Universal Data Access of Structured Data -- 10.12 The SQL XML Data Structure Connection -- 10.13 Conclusion -- 11 Outer Join Optimization -- 11.1 Join Table Reordering -- 11.2 Dynamic Shortening of the Access Path -- 11.3 Removal of Unnecessary Tables From Outer Join View -- 11.4 Increased Efficiency of Parallel Database Processing -- 11.5 Dynamic Rebuild to Pick Up New SQL Features -- 11.6 Optimization of Nonrelational SQL Interfaces -- 11.7 Applying Hierarchical Optimizations to Network Structures -- 11.8 Shifting ON Clauses to the WHERE Clause -- 11.9 Conclusion -- 12 Hierarchical Relational Processor Prototype -- 12.1 Hierarchical Relational Prototype Operation -- 12.2 Basic Data Modeling -- 12.3 Many-to-Many Relationships -- 12.4 Embedded Views -- 12.5 View Optimization -- 12.6 Conclusion -- 13 Object/Relational Interface -- 13.1 Standardized SQL Interface -- 13.2 Data Modeling and Structure Processing -- 13.3 Data Abstraction and Reusability -- 13.4 Data Inheritance -- 13.5 Database Navigation, Efficiency, and Nonrelational Access -- 13.6 Late Binding and Polymorphism -- 13.7 Plug and Play -- 13.8 Conclusion -- 14 Nonrelational SQL-Based Universal Data Access -- 14.1 Structured Record Overview -- 14.2 SQL Structured Data Access Basics.
14.3 Internal Navigation and Mapping of Structured Data -- 14.4 SQL-Based Universal Data Access of Structured Data -- 14.5 Handling Multiple Structure Formats Within a File -- 14.6 Interfacing to Prerelational and Postrelational Data -- 14.7 The Importance of the View for Contiguous Data -- 14.8 Conclusion -- Part IV: Advanced Data Structure Processing Capabilities -- 15 Advanced Lower Structure Linking -- 15.1 Overview of Nonroot Lower Level Linking -- 15.2 Previous Nonroot Lower Level Linking Method -- 15.3 Semantics of Nonroot Lower Level Linking -- 15.4 Single Path Reference to Lower Structure -- 15.5 Multiple Path References to Lower Structure -- 15.6 Optimization Concerns for Nonroot Lower Level Linking -- 15.7 Using Lower Structure Linking with a View WHERE Clause -- 15.8 Conclusion -- 16 Dynamic Structure Combining by Joining, Mashups, and Association -- 16.1 Static Structure Join -- 16.2 Dynamic Structure Join -- 16.3 Heterogeneous Join -- 16.4 Access Path Data Filtering -- 16.5 Natural View Nesting -- 16.6 Simple Mashup -- 16.7 Complex Mashup -- 16.8 Combining Structures with Association Tables -- 16.9 More Complex Association Table Usage -- 16.10 Conclusion -- 17 Dynamically Increasing Data Value and Flexibility -- 17.1 Data Structure Modeling of Single-Path Structures -- 17.1.1 Structure Modeling Vertical Growth -- 17.1.2 Structure Modeling Depth Growth -- 17.2 Data Structure Modeling of Multiple-Path Processing -- 17.3 Static Data Joining of Structures -- 17.4 Dynamic Data Joining of Structures -- 17.5 Logical Data Structure Advantage -- 17.6 Multipath Data Qualification -- 17.7 Dynamic Path Data Filtering -- 17.8 Miscellaneous Operations that Increase the Data Value -- 17.8.1 Structure-Aware Processing -- 17.8.2 Hierarchical Optimization -- 17.8.3 Increase of Data Accuracy and Correctness -- 17.8.4 Interactive Data Access.
17.8.5 Automatic Data Aggregation -- 17.9 Conclusion -- 18 Automatic Multipath Hierarchical Structure Operations -- 18.1 Structure-Aware Processing -- 18.2 Hierarchical Optimization -- 18.3 Focused Aggregated Data Retrieval -- 18.4 Multipath Hierarchical Processing -- 18.4.1 LCA Processing -- 18.4.2 LCA Type 1 Internal Processing -- 18.4.3 LCA Type 2 Internal Processing -- 18.4.4 LCA Type 2 Variable OR Processing -- 18.4.5 Multiple LCA Type 1 Processing -- 18.4.6 Combining Processing of LCA Types 1 and 2 -- 18.5 Nonlinear Ordering -- 18.6 Global Views and Schema-Free Processing -- 18.7 Global Queries and Hierarchical Data Filtering -- 18.8 Automatic Hierarchical Parallel Processing -- 18.9 Conclusion -- 19 Variable Data Structure Generation -- 19.1 Variable Data Structure Generation Is a Powerful Concept -- 19.2 Linking Below the Root Increases Structure Joining -- 19.3 Looking Backward and Forward -- 19.3.1 Looking Backward -- 19.3.2 Looking Forward -- 19.4 Advanced Variable Structure Control -- 19.5 Flexible Multiple Generation Choices -- 19.5.1 One or the Other Variable Test -- 19.5.2 Multiple Independent Tests -- 19.6 Nested and Embedded Variable Structure Creation -- 19.6.1 Nested Variable Structure Test -- 19.6.2 Embedded Variable Structure Test -- 19.7 Variable Structure Generation Along Multiple Paths -- 19.8 Variable Structure Range Filtering -- 19.9 Why Variable Structures Work with Hierarchical Data -- 19.10 Conclusion -- 20 Semantically Controlled Data Structure Transformations -- 20.1 Restructuring and Reshaping -- 20.1.1 Restructuring -- 20.1.2 Restructuring Using Multiple Levels -- 20.2 Reshaping -- 20.2.1 Inverting a Linear Structure by Reshaping -- 20.2.2 Linear-to-Nonlinear Reshaping -- 20.2.3 Nonlinear-to-Linear Reshaping -- 20.2.4 Nonlinear-to-Nonlinear Reshaping -- 20.3 Data Structure Virtualization.
20.3.1 Data Fragment Control.
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Intro -- Advanced Standard SQL Dynamic Structured Data Modeling and Hierarchical Processing -- Contents -- Preface -- Introduction -- Part I: The Basics of the RelationalJoin Operation -- 1 Relational Join Introduction -- 1.1 Standard Inner Join Review -- 1.2 Problems with Relational Join Processing -- 1.3 Outer Join Review -- 1.4 Problems with Previous Outer Join Syntax -- 1.5 Conclusion -- 2 The Standard SQL Join Operation -- 2.1 Standard SQL Join Syntax -- 2.2 Standard SQL Join Operation -- 2.3 Standard SQL Join Does Not Follow the Cartesian Product Model -- 2.4 Determining Standard SQL Join Associativity and Commutativity -- 2.5 What Outer Join Commutativity Is -- 2.6 What Outer Join Associativity Is -- 2.7 Hierarchictivity in Addition to Associativity and Commutativity -- 2.8 Conclusion -- 3 Standard SQL Join Types and Their Operation -- 3.1 FULL Outer Join -- 3.2 One-Sided Outer Join -- 3.3 INNER Join -- 3.4 CROSS Join -- 3.5 UNION Join -- 3.6 Intermixing Join Types -- 3.7 Conclusion -- 4 Natural Joins -- 4.1 Explicit and Implicit Natural Joins -- 4.2 Multitable Natural Outer Joins -- 4.3 Natural One-Sided Outer Join -- 4.4 Natural FULL Outer Join -- 4.5 Natural Inner Joins -- 4.6 Intermixing Natural Join Types -- 4.7 Natural One-Sided Join Transformation -- 4.8 Conclusion -- Part II: Outer Join Data Modeling and Structured Processing -- 5 Data Structure Review -- 5.1 The Power of Hierarchical Data Structures -- 5.2 Three-Tier Database Architecture -- 5.3 External and Internal Views -- 5.4 Conceptual View -- 5.5 Many-to-One and One-to-Many Relationships -- 5.6 Many-to-Many Relationships -- 5.7 Converting Network Structures to Hierarchical Structures -- 5.8 Relating Hierarchical Processing to Relational Processing -- 5.9 Physical Versus Logical Data Structures -- 5.10 Sibling Legs Query Semantics.

5.11 Ordering of Data Structures Can Cause Their Restructuring -- 5.12 Data Structure Composition -- 5.13 Good Data Modeling Design Principles -- 5.14 Conclusion -- 6 Outer Join Does Data Modeling -- 6.1 SQL Data Modeling Using the Outer Join -- 6.2 ON Clause Data Modeling Join Condition Rules -- 6.3 Valid and Invalid ON Clause Data Modeling Examples -- 6.4 Valid and Invalid Data Modeling Results -- 6.5 Substructure Views -- 6.6 WHERE Clause Filtering with Data Structures -- 6.7 WHERE Clause Filtering with Substructures -- 6.8 Complex Data Modeling Example -- 6.9 Conclusion -- 7 Outer Join Data Modeling-Related Capabilities -- 7.1 Data Structure Filtering -- 7.2 Indirect Structure Linking -- 7.3 Nonhierarchical Join Type Support -- 7.4 Nonhierarchical Joining of Data Structures -- 7.5 Many-to-Many Data Modeling and Intersecting Data -- 7.6 Conclusion -- 8 More About Outer Join Data Modeling -- 8.1 Importance of SQL's Inherent Data Structure Processing Ability -- 8.2 Efficient Client/Server Data Structure Processing -- 8.3 Coding Data Modeling Outer Join Statements -- 8.4 Generation of Data Modeling Outer Join Statements -- 8.5 Hierarchical Data Structure Processing Empirical Proof -- 8.5.1 Hierarchical Control -- 8.5.2 Structure Control -- 8.6 Nonhierarchical Data Structure Processing Empirical Proo -- 8.7 Embedded Structured View Support Empirical Proof -- 8.8 Indirect Link Empirical Proof -- 8.9 SQL:1999 and Data Modeling -- 8.10 What Makes the ANSI Standard Outer Join Unique for Data Modeling -- 8.11 Data Modeling with Old-Style Outer Joins -- 8.12 The New Role of the Inner Join Operation -- 8.13 Conclusion -- Part III: New Capabilities Based on Outer JoinData Modeling -- 9 Data Structure Extraction (DSE) Technology -- 9.1 Extracting Data Structure Information From the Outer Join -- 9.2 DSE Example -- 9.3 Logical Table Example.

9.4 Symmetric Linking of Data Structures Example -- 9.5 DSE Internal Logic -- 9.6 Why Vendors Need the DSE Technology -- 9.7 DSE Avoids Imposing Data Structures on SQL -- 9.8 Conclusion -- 10 Outer Join Advanced Capabilities -- 10.1 Database Navigation -- 10.2 Access Optimizations -- 10.3 Enterprise and Legacy Database Access -- 10.4 Open Database Access Interface -- 10.5 Seamless Value-Added Features -- 10.6 Data Warehouse Interface -- 10.7 Hierarchical Relational Processing -- 10.8 Object Relational Interface -- 10.9 View Update Capability -- 10.10 Multimedia Application Directory Support -- 10.11 Universal Data Access of Structured Data -- 10.12 The SQL XML Data Structure Connection -- 10.13 Conclusion -- 11 Outer Join Optimization -- 11.1 Join Table Reordering -- 11.2 Dynamic Shortening of the Access Path -- 11.3 Removal of Unnecessary Tables From Outer Join View -- 11.4 Increased Efficiency of Parallel Database Processing -- 11.5 Dynamic Rebuild to Pick Up New SQL Features -- 11.6 Optimization of Nonrelational SQL Interfaces -- 11.7 Applying Hierarchical Optimizations to Network Structures -- 11.8 Shifting ON Clauses to the WHERE Clause -- 11.9 Conclusion -- 12 Hierarchical Relational Processor Prototype -- 12.1 Hierarchical Relational Prototype Operation -- 12.2 Basic Data Modeling -- 12.3 Many-to-Many Relationships -- 12.4 Embedded Views -- 12.5 View Optimization -- 12.6 Conclusion -- 13 Object/Relational Interface -- 13.1 Standardized SQL Interface -- 13.2 Data Modeling and Structure Processing -- 13.3 Data Abstraction and Reusability -- 13.4 Data Inheritance -- 13.5 Database Navigation, Efficiency, and Nonrelational Access -- 13.6 Late Binding and Polymorphism -- 13.7 Plug and Play -- 13.8 Conclusion -- 14 Nonrelational SQL-Based Universal Data Access -- 14.1 Structured Record Overview -- 14.2 SQL Structured Data Access Basics.

14.3 Internal Navigation and Mapping of Structured Data -- 14.4 SQL-Based Universal Data Access of Structured Data -- 14.5 Handling Multiple Structure Formats Within a File -- 14.6 Interfacing to Prerelational and Postrelational Data -- 14.7 The Importance of the View for Contiguous Data -- 14.8 Conclusion -- Part IV: Advanced Data Structure Processing Capabilities -- 15 Advanced Lower Structure Linking -- 15.1 Overview of Nonroot Lower Level Linking -- 15.2 Previous Nonroot Lower Level Linking Method -- 15.3 Semantics of Nonroot Lower Level Linking -- 15.4 Single Path Reference to Lower Structure -- 15.5 Multiple Path References to Lower Structure -- 15.6 Optimization Concerns for Nonroot Lower Level Linking -- 15.7 Using Lower Structure Linking with a View WHERE Clause -- 15.8 Conclusion -- 16 Dynamic Structure Combining by Joining, Mashups, and Association -- 16.1 Static Structure Join -- 16.2 Dynamic Structure Join -- 16.3 Heterogeneous Join -- 16.4 Access Path Data Filtering -- 16.5 Natural View Nesting -- 16.6 Simple Mashup -- 16.7 Complex Mashup -- 16.8 Combining Structures with Association Tables -- 16.9 More Complex Association Table Usage -- 16.10 Conclusion -- 17 Dynamically Increasing Data Value and Flexibility -- 17.1 Data Structure Modeling of Single-Path Structures -- 17.1.1 Structure Modeling Vertical Growth -- 17.1.2 Structure Modeling Depth Growth -- 17.2 Data Structure Modeling of Multiple-Path Processing -- 17.3 Static Data Joining of Structures -- 17.4 Dynamic Data Joining of Structures -- 17.5 Logical Data Structure Advantage -- 17.6 Multipath Data Qualification -- 17.7 Dynamic Path Data Filtering -- 17.8 Miscellaneous Operations that Increase the Data Value -- 17.8.1 Structure-Aware Processing -- 17.8.2 Hierarchical Optimization -- 17.8.3 Increase of Data Accuracy and Correctness -- 17.8.4 Interactive Data Access.

17.8.5 Automatic Data Aggregation -- 17.9 Conclusion -- 18 Automatic Multipath Hierarchical Structure Operations -- 18.1 Structure-Aware Processing -- 18.2 Hierarchical Optimization -- 18.3 Focused Aggregated Data Retrieval -- 18.4 Multipath Hierarchical Processing -- 18.4.1 LCA Processing -- 18.4.2 LCA Type 1 Internal Processing -- 18.4.3 LCA Type 2 Internal Processing -- 18.4.4 LCA Type 2 Variable OR Processing -- 18.4.5 Multiple LCA Type 1 Processing -- 18.4.6 Combining Processing of LCA Types 1 and 2 -- 18.5 Nonlinear Ordering -- 18.6 Global Views and Schema-Free Processing -- 18.7 Global Queries and Hierarchical Data Filtering -- 18.8 Automatic Hierarchical Parallel Processing -- 18.9 Conclusion -- 19 Variable Data Structure Generation -- 19.1 Variable Data Structure Generation Is a Powerful Concept -- 19.2 Linking Below the Root Increases Structure Joining -- 19.3 Looking Backward and Forward -- 19.3.1 Looking Backward -- 19.3.2 Looking Forward -- 19.4 Advanced Variable Structure Control -- 19.5 Flexible Multiple Generation Choices -- 19.5.1 One or the Other Variable Test -- 19.5.2 Multiple Independent Tests -- 19.6 Nested and Embedded Variable Structure Creation -- 19.6.1 Nested Variable Structure Test -- 19.6.2 Embedded Variable Structure Test -- 19.7 Variable Structure Generation Along Multiple Paths -- 19.8 Variable Structure Range Filtering -- 19.9 Why Variable Structures Work with Hierarchical Data -- 19.10 Conclusion -- 20 Semantically Controlled Data Structure Transformations -- 20.1 Restructuring and Reshaping -- 20.1.1 Restructuring -- 20.1.2 Restructuring Using Multiple Levels -- 20.2 Reshaping -- 20.2.1 Inverting a Linear Structure by Reshaping -- 20.2.2 Linear-to-Nonlinear Reshaping -- 20.2.3 Nonlinear-to-Linear Reshaping -- 20.2.4 Nonlinear-to-Nonlinear Reshaping -- 20.3 Data Structure Virtualization.

20.3.1 Data Fragment Control.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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