Based on the Artech House classic ANSI SQL Data Modeling and Structure Processing, this expanded and updated book offers you an essential tool for utilizing the ANSI SQL outer join operation to perform simple or complex hierarchical data modeling and structure processing. The book provides you with a comprehensive review of the outer join operation, its powerful syntax and semantics, and new features and capabilities. This revised resource introduces several important new concepts such as relationship and hierarchical integration at the hierarchical processing level, multipath hierarchical automatic XML query processing, dynamic structured data processing using automatic metadata maintenance, and advanced data transformations. Featuring more than 230 illustrations, the book shows you how to tap the full power of data structure extraction technology that gathers data structure meta information naturally embedded in ANSI SQL specifications. You discover existing, but previously unknown, SQL capabilities for improving performance. The book explains how to perform multitable outer joins and combine relational structures with hierarchical structures. Moreover you learn how to establish a default database standard for hierarchical data modeling and structure processing.
Relational Join Introduction - Standard Inner Join Review. Problems with Relational Join Processing. Outer Join Review. Problems with Previous Outer Join Syntax. Conclusion.; The Standard SQL Join Operation - Standard SQL Join Syntax. Standard SQL Join Operation. Standard SQL Join Does Not Follow the Cartesian Product Model. Determining Standard SQL Join Associativity and Commutativity. What Outer Join Commutativity Is. What Outer Join Associativity Is. Hierarchictivity in Addition to Associativity and Commutativity. Conclusion.; Standard SQL Join Types and Their Operation - FULL Outer Join. One-Sided Outer Join. INNER Join. CROSS Join. UNION Join. Intermixing Join Types. Conclusion.; Natural Joins - Explicit and Implicit Natural Joins. Multitable Natural Outer Joins. Natural One-Sided Outer Join. Natural FULL Outer Join. Natural Inner Joins. Intermixing Natural Join Types. Natural One-Sided Join Transformation. Conclusion.; Part II: Outer Join Data Modeling and Structured Processing; Data Structure Review - The Power of Hierarchical Data Structures. Three-Tier Database Architecture. External and Internal Views. Conceptual View. Many-to-One and One-to-Many Relationships. Many-to-Many Relationships. Converting Network Structures to Hierarchical Structures. Relating Hierarchical Processing to Relational Processing. Physical Versus Logical Data Structures. Sibling Legs Query Semantics. Ordering of Data Structures Can Cause Their Restructuring. Data Structure Composition. Good Data Modeling Design Principles. Conclusion.; Outer Join Does Data Modeling - SQL Data Modeling Using the Outer Join. ON Clause Data Modeling Join Condition Rules. Valid and Invalid ON Clause Data Modeling Examples. Valid and Invalid Data Modeling Results. Substructure Views. WHERE Clause Filtering with Data Structures. WHERE Clause Filtering with Substructures. Complex Data Modeling Example. Conclusion.; Outer Join Data Modeling-Related Capabilities - Data Structure Filtering. Indirect Structure Linking. Nonhierarchical Join Type Support. Nonhierarchical Joining of Data Structures. Many-to-Many Data Modeling and Intersecting Data. Conclusion.; More About Outer Join Data Modeling - Importance of SQL 's Inherent Data Structure Processing Ability. Efficient Client/Server Data Structure Processing. Coding Data Modeling Outer Join Statements. Generation of Data Modeling Outer Join Statements. Hierarchical Data Structure Processing Empirical Proof. Nonhierarchical Data Structure Processing Empirical Proof. Embedded Structured View Support Empirical Proof. Indirect Link Empirical Proof. SQL:1999 and Data Modeling. What Makes the ANSI Outer Join Unique for Data Modeling. Data Modeling with Old-Style Outer Joins. The New Role of the Inner Join Operation. Conclusion.; Part III: New Capabilities Based on Outer Join Data Modeling; Data Structure Extraction (DSE) Technology Extracting Data Structure Information From the Outer Join. DSE Example. Logical Table Example. Symmetric Linking of Data Structures Example. DSE Internal Logic. Why Vendors Need the DSE Technology. DSE Avoids Imposing Data Structures on SQL. Conclusion. ; Outer Join Advanced Capabilities - Database Navigation. Access Optimizations. Enterprise and Legacy Database Access. Open Database Access Interface. Seamless Value-Added Features. Data Warehouse Interface. Hierarchical Relational Processing. Object Relational Interface. View Update Capability. Multimedia Application Directory Support. Universal Data Access of Structured Data. The SQL XML Data Structure Connection. Conclusion.; Outer Join Optimization - Join Table Reordering. Dynamic Shortening of the Access Path. Removal of Unnecessary Tables From Outer Join View. Increased Efficiency of Parallel Database Processing. Dynamic Rebuild to Pick Up New SQL Features. Optimization of Nonrelational SQL Interfaces. Applying Hierarchical Optimizations to Network Structures. Shifting ON Clauses to the WHERE Clause. Conclusion.; Hierarchical Relational Processor Prototype - Hierarchical Relational Prototype Operation. Basic Data Modeling. Many-to-Many Relationships. Embedded Views. View Optimization. Conclusion.; Object/Relational Interface - Standardized SQL Interface. Data Modeling and Structure Processing. Data Abstraction and Reusability. Data Inheritance. Database Navigation, Efficiency, and Nonrelational Access. Late Binding and Polymorphism. Plug and Play. Conclusion.; Nonrelational SQL-Based Universal Data Access - Structured Record Overview. SQL Structured Data Access Basics. Internal Navigation and Mapping of Structured Data. SQL-Based Universal Data Access of Structured Data. Handling Multiple Structure Formats Within a File. Interfacing to Prerelational and Postrelational Data. The Importance of the View for Contiguous Data. Conclusion.; Part IV: Advanced Data Structure Processing Capabilities; Advanced Lower Structure Linking - Overview of Nonroot Lower Level Linking. Previous Nonroot Lower Level Linking Method. Semantics of Nonroot Lower Level Linking. Single Path Reference to Lower Structure. Multiple Path References to Lower Structure. Optimization Concerns for Nonroot Lower Level Linking. Using Lower Structure Linking With a View WHERE Clause. Conclusion.; Dynamic Structure Combining by Joining, Mashups, and Association - Static Structure Join. Dynamic Structure Join. Heterogeneous Join. Access Path Data Filtering. Natural View Nesting. Simple Mashup. Complex Mashup. Combining Structures with Association Tables. More Complex Association Table Usage. Conclusion.; Dynamically Increasing Data Value and Flexibility - Data Structure Modeling of Single-Path Structures. Data Structure Modeling of Multiple-Path Processing. Static Data Joining of Structures. Dynamic Data Joining of Structures. Logical Data Structure Advantage. Multipath Data Qualification. Dynamic Path Data Filtering. Miscellaneous Operations that Increase the Data Value. Conclusion. ; Automatic Multipath Hierarchical Structure Operations - Structure-Aware Processing. Hierarchical Optimization. Focused Aggregated Data Retrieval. Multipath Hierarchical Processing. Nonlinear Ordering. Global Views and Schema-Free Processing. Global Queries and Hierarchical Data Filtering. Automatic Hierarchical Parallel Processing. Conclusion. ; Variable Data Structure Generation - Variable Data Structure Generation Is a Powerful Concept. Linking Below the Root Increases Structure Joining. Looking Backward and Forward. Advanced Variable Structure Control. Flexible Multiple Generation Choices. Nested and Embedded Variable Structure Creation. Variable Structure Generation Along Multiple Paths. Variable Structure Range Filtering. Why Variable Structures Work with Hierarchical Data. Conclusion.; Semantically Controlled Data Structure Transformations - Restructuring and Reshaping. Reshaping. Data Structure Virtualization. Polymorphic Transformation. Multipath Queries Alternative to Transformations. Conclusion.; Automatic Processing of Remote Dynamic Structured Data - Static Versus Dynamic Structured Data. Automatic Processing of Remote Dynamic Structured Data. Dynamic Structured Data Processing Example. Integrating SQL with Dynamic Structured Data Maintenance. Different Levels of Metadata Processing. Structured Data Processing Collaboration. SQL Hierarchical Processing for Structured Data Collaboration. Conclusion.; Part V: SQL Transparent XML Hierarchical Multipath Query Processor; New SQL Hierarchical Processing Technology and Discoveries - External Versus Internal SQL. Hierarchical Processing. Hierarchical Processing Background History. Hierarchical Principles and Operation. Schema-Free Navigationless Hierarchical Database Access. Focused Aggregated Data Retrieval. Combing Relational and Hierarchical Advantages. Global Hierarchical Optimization. SQL Multipath Multioccurrence Data Filtering. Multipath LCA Types of Processing. Isolating and Manipulating Data Segments. Linking Below Root. SQL Data Transformations. Conclusion.; SQL/XML: Operation, Politics, Ramifications, and Solution - XML Data Description and Operation. Politics of SQL, XML, and the Secret Agenda. Further Effects of the Secret SQL/XML Agenda. A Better SQL/XML Solution Using Standard SQL is Possible. Conclusion. ; SQL Hierarchical XML Processor Operation - Mapping Relational Hierarchical Structure to Hierarchical Relational Rowset. Mapping Physical XML Hierarchical Structure to Hierarchical Relational Rowset. SQL Hierarchical Query Specification with Data Filtering. SQL Hierarchical Processor Internal Layout. SQL Hierarchical XML Processor External Operations. SQL Hierarchical XML Processor Operations. Conclusion.; SQL Hierarchical XML Processor Examples - Node Selection with SQL SELECT Operation. Multipath Hierarchical Data Filtering using WHERE Clause. Simple Multipath Nonlinear Data Qualification. Complex Multipath Nonlinear Data Qualification. Backward Path Data Filtering. Advanced Structure Linking with Data Mashups. Dynamic Variable Structure Generation Control. Conclusion.; Summary ;
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Michael M. David
Michael David is a principal at CompuAid in Santa Monica, California. He has been a consulting staff scientist with Teradata Corp., a senior software designer at Sterling Software's Answer Division, and has been designing commercial database tools and utilities for over fifteen years.
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Lee Fesperman
Lee Fesperman is the president of FirstSQL Software in El Cerrito, California. He received his B.A. in English from Birmingham Southern College.