Advanced Standard SQL Dynamic Structured Data Modeling and Hierarchical Processing

Name: Advanced Standard SQL Dynamic Structured Data Modeling and Hierarchical Processing
Brand: Artech House
Price: 120.99 EUR
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Michael M. David(Author)

Artech House (Publisher)

1st Edition

Published on 1. January 2013

380 pages

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978-1-60807-534-8 (ISBN)

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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
20.3.2 Data Virtualization Example
20.4 Polymorphic Transformation
20.4.1 Polymorphic Linear Example
20.4.2 Polymorphic Nonlinear Example
20.5 Multipath Queries Alternative to Transformations
20.6 Conclusion
21 Automatic Processing of Remote Dynamic Structured Data
21.1 Static Versus Dynamic Structured Data
21.2 Automatic Processing of Remote Dynamic Structured Data
21.3 Dynamic Structured Data Processing Example
21.4 Integrating SQL with Dynamic Structured Data Maintenance
21.5 Different Levels of Metadata Processing
21.6 Structured Data Processing Collaboration
21.7 SQL Hierarchical Processing for Structured Data Collaboration
21.8 Conclusion
22 New SQL Hierarchical Processing Technology and Discoveries
22.1 External Versus Internal SQL Hierarchical Processing
22.2 Hierarchical Processing Background History
22.3 Hierarchical Principles and Operation
22.4 Schema-Free Navigationless Hierarchical Database Access
22.5 Focused Aggregated Data Retrieval
22.6 Combing Relational and Hierarchical Advantages
22.7 Global Hierarchical Optimization
22.8 SQL Multipath Multioccurrence Data Filtering
22.9 Multipath LCA Types of Processing
22.9.1 WHERE Clause LCA Processing
22.9.2 SELECT Operation LCA Processing
22.10 Isolating and Manipulating Data Segments
22.11 Linking Below Root
22.12 SQL Data Transformations
22.13 Conclusion
Part V: SQL Transparent XML Hierarchical Multipath Query Processor
23 SQL/XML: Operation, Politics,Ramifications, and Solution
23.1 XML Data Description and Operation
23.1.1 Semistructured Data
23.1.2 Multiple Content Types
23.1.3 Variable Structure Formats
23.1.4 Duplicate Element Use
23.1.5 Shared Element Data
23.1.6 XML Navigation
23.1.7 Namespaces
23.1.8 Recursive Structures
23.1.9 Ordered Data
23.1.10 XML Data Processing
23.2 Politics of SQL, XML, and the Secret Agenda
23.2.1 SQL/XML Standard and XQuery Decisions Limit Capabilities
23.2.2 XQuery's Decision to Also Support Relational Processing
23.2.3 Limiting Hierarchical Support to Single-Path Processing
23.2.4 Ignoring Navigationless Schema-Free Access Support
23.2.5 Not Utilizing Standard SQL's Natural Hierarchical Processing
23.3 Further Effects of the Secret SQL/XML Agenda
23.3.1 SQL/XML Vendor Solutions are Proprietary and Incompatible
23.3.2 XQuery and SQL/XML Standard Favors Semi-structured Processing
23.3.3 XML Processing Today Is Limited by User's Linear Mindset
23.3.4 XQuery Does Not Support SQL's Powerful SELECT Operator
23.4 A Better SQL/XML Solution Using Standard SQL is Possible
23.4.1 The SQL Hierarchical XML Solution Stays Naturally within SQL
23.4.2 XML-Centric Syntax Additions Are Unnecessary
23.5 Conclusion
24 SQL Hierarchical XML Processor Operation
24.1 Mapping Relational Hierarchical Structure to Hierarchical Relational Rowset
24.2 Mapping Physical XML Hierarchical Structure to Hierarchical Relational Rowset
24.3 SQL Hierarchical Query Specification with Data Filtering
24.4 SQL Hierarchical Processor Internal Layout
24.5 SQL Hierarchical XML Processor External Operations
24.6 SQL Hierarchical XML Processor Operations
24.6.1 Preprocessor
24.6.2 Standard SQL Processor
24.6.3 Asynchronous Access Processor
24.6.4 Postprocessor
24.7 Conclusion
25 SQL Hierarchical XML Processor Examples
25.1 Node Selection with SQL SELECT Operation
25.1.1 Selecting a Single Linear Path
25.1.2 Node Promotion with Single Path
25.1.3 Node Collection with Multiple Paths
25.1.4 Selecting Structure Fragments
25.2 Multipath Hierarchical Data Filtering using WHERE Clause
25.2.1 Downward Path Data Qualification
25.2.2 Upward Path Data Qualification
25.2.3 Bidirectional Data Qualification
25.3 Simple Multipath Nonlinear Data Qualification
25.3.1 LCA Many-to-One Result Data Qualification
25.3.2 LCA One-to-Many Result Data Qualification
25.3.3 LCA Can be Located Higher than Parent
25.3.4 LCA Data from Up and Down the Structure
25.3.5 Multiple LCAs
25.4 Complex Multipath Nonlinear Data Qualification
25.4.1 LCA Determines Range of Combinations for Decision Logic
25.4.2 LCA Data Combinations are Controlled by Data Occurrence
25.4.3 Variable LCAs with OR Decision Logic
25.4.4 Complex Multipath LCA Decision Logic
25.4.5 LCA Logic too Complex to Hand Code
25.5 Backward Path Data Filtering
25.5.1 Static Backward Path Data Filtering
25.5.2 Dynamic Backward Path Qualification
25.6 Advanced Structure Linking with Data Mashups
25.6.1 Hierarchical Structure Linking
25.6.2 Linking Below Root of Lower Structure with Root Selected
25.6.3 Linking Below Root of Lower Structure without Root Selected
25.6.4 Filtering Below Root of Lower View with Qualification
25.7 Dynamic Variable Structure Generation Control
25.7.1 Variable Structure Generation Controlled at the Node Level
25.7.2 Variable Structure Generation Controlled at the View Level
25.8 Conclusion
26 Summary
Appendix A: Database Relationships and Views Used in This Book
Notes on the Database Views
Glossary
Bibliography
About the Authors
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Advanced Standard SQL Dynamic Structured Data Modeling and Hierarchical Processing

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