Database Systems: The Complete Book, 2/e (IE-Paperback)

描述

適用於計算機科學系的大學部、研究生課程中的資料庫系統和資料庫設計與應用課程。

這本由知名計算機科學家撰寫的資料庫系統介紹書提供了全面的方法，著重於資料庫設計、資料庫使用以及資料庫應用和資料庫管理系統的實現。

本書的前半部分從資料庫設計師、使用者和應用程式開發人員的角度深入探討了資料庫。它涵蓋了最新的資料庫標準SQL:1999、SQL/PSM、SQL/CLI、JDBC、ODL和XML，比大多數其他教材更廣泛地涵蓋了SQL。本書的後半部分從DBMS實現者的角度深入探討了資料庫。它專注於存儲結構、查詢處理和事務管理。本書涵蓋了這些領域的主要技術，比大多數其他教材更廣泛地涵蓋了查詢優化，還包括多維和位圖索引、分佈式事務和信息集成技術等高級主題。

資源：

• 開放存取作者網站 ¿http://infolab.stanford.edu/~ullman/dscb.html¿ 包括PowerPoint幻燈片、教學筆記、作業、專案、Oracle編程指南以及選定練習的解答。


• 僅限教師使用的Pearson資源：完整解答手冊（點擊上方的資源標籤以查看可下載的文件）

特點

• 
  許多實際例子。
  
  
  • 
    

    提供易讀且引人入勝的介紹。

    
    
  
  
  
  


• 
  詳細介紹資料庫建模–包括詳細且獨立的解釋如何使用E/R和ODL設計資料庫。
  
  
  
  • 
    

    教授這個重要的計劃過程的第一步。

    
    
  
  
  
  


• 
  優秀、最新且詳細的SQL涵蓋範圍–包括對物件關聯系統和新SQL:1999標準的許多方面的涵蓋。
  
  
  
  • 
    

    提供比市場上其他書籍更廣泛的查詢處理介紹。

    
    
  
  
  
  


• 
  討論將資料庫編程與C或Java代碼相連接的技術–包括SQL/PSM、SQL/CLI和JDBC的討論。
  
  
  
  • 
    

    為學生提供將最新技術與資料庫整合的實用建議。

    
    
  
  
  
  


• 
  涵蓋對資料庫設計師和使用者重要的高級問題。
  
  
  
  • 
    

    包括對SQL:1999中的視圖、完整性約束、斷言、觸發器、事務、授權和遞迴的討論。

    
    
  
  
  
  


• 
  討論在構建資料庫應用程序之前如何成功規劃。
  
  
  
  • 
    

    反映了這些計劃在現實世界中的開發方式。

    
    
  
  
  
  


• 
  涵蓋設計存儲結構和實現各種索引方案等主題。
  
  
  
  • 
    

    向學生展示如何構建高效的資料庫管理系統。

    
    
  
  
  
  


• 
  廣泛涵蓋查詢處理和優化。
  
  
  
  • 
    

    向學生展示如何調整資料庫系統以提高性能。

    
    
  
  
  
  


• 
  全面涵蓋並發控制和恢復的事務處理機制，包括分佈式和長時間事務。
  
  
  
  • 
    

    展示如何設計能處理現實世界業務應用的複雜資料庫系統。

    
    
  
  
  
  


• 
  涵蓋信息集成，包括數據倉庫、中介、OLAP、數據立方系統和數據挖掘。
  
  
  

Hector Garcia-Molina is the L. Bosack and S. Lerner Professor of Computer Science and Electrical Engineering at Stanford University. His research interests include digital libraries, information integration, and database applications on the Internet. He was a recipient of the SIGMOD Innovations Award and a member of PITAC (President's Information-Technology Advisory Council). He currently serves on the Board of Directors of Oracle Corp.

Jeffrey D. Ullman is the Stanford W. Ascherman Professor of Computer Science (emeritus) at Stanford University. He is the author or co-author of 16 books, including Elements of ML Programming (Prentice Hall 1998). His research interests include data mining, information integration, and electronic education. He is a member of the National Academy of Engineering, and recipient of a Guggenheim Fellowship, the Karl V. Karlstom Outstanding Educator Award, the SIGMOD Contributions and Edgar F. Codd Innovations Awards, and the Knuth Prize.
 

Jennifer Widom is Professor of Computer Science and Electrical Engineering at Stanford University. Her research interests span many aspects of nontraditional data management. She is an ACM Fellow and a member of the National Academy of Engineering, she received the ACM SIGMOD Edgar F. Codd Innovations award in 2007 and was a Guggenheim Fellow in 2000, and she has served on a variety of program committees, advisory boards, and editorial boards.

Hector Garcia-Molina是斯坦福大學的L. Bosack和S. Lerner計算機科學和電氣工程教授。他的研究興趣包括數字圖書館、信息集成和互聯網上的數據庫應用。他曾獲得SIGMOD創新獎，並擔任過PITAC（總統信息技術諮詢委員會）成員。他目前擔任Oracle Corp.董事會成員。

Jeffrey D. Ullman是斯坦福大學的W. Ascherman計算機科學名譽教授。他是16本書的作者或合著者，包括《ML編程元素》（Prentice Hall 1998）。他的研究興趣包括數據挖掘、信息集成和電子教育。他是國家工程院的成員，並獲得了古根海姆獎學金、卡爾·卡爾斯頓傑出教育家獎、SIGMOD貢獻獎、Edgar F. Codd創新獎和Knuth獎。

Jennifer Widom是斯坦福大學的計算機科學和電氣工程教授。她的研究興趣涵蓋非傳統數據管理的多個方面。她是ACM院士和國家工程院的成員，她於2007年獲得了ACM SIGMOD Edgar F. Codd創新獎，並於2000年獲得了古根海姆獎學金，她曾擔任過多個程序委員會、諮詢委員會和編輯委員會的成員。

1 The Worlds of Database Systems
   1.1 The Evolution of Database Systems
   1.1.1 Early Database Management Systems
   1.1.2 Relational Database Systems
   1.1.3 Smaller and Smaller Systems
   1.1.4 Bigger and Bigger Systems
   1.1.5 Information Integration
   1.2 Overview of a Database Management System
   1.2.1 Data-Definition Language Commands
   1.2.2 Overview of Query Processing
   1.2.3 Storage and Buffer Management
   1.2.4 Transaction Processing
   1.2.5 The Query Processor
   1.3 Outline of Database-System Studies
   1.4 References for Chapter 1

PART I: Relational Database Modeling
2 The Relational Model of Data
   2.1 An Overview of Data Models
   2.1.1 What is a Data Model?
   2.1.2 Important Data Models
   2.1.3 The Relational Model in Brief
   2.1.4 The Semistructured Model in Brief
   2.1.5 Other Data Model
   2.1.6 Comparison of Modeling Approaches
   2.2 Basics of the Relational Model
   2.2.1 Attributes
   2.2.2 Schemas
   2.2.3 Tuples
   2.2.4 Domains
   2.2.5 Equivalent Representations of a Relation
   2.2.6 Relation Instances
   2.2.7 Keys of Relations
   2.2.8 An Example Database Schema
   2.2.9 Exercises for Section 2.2
   2.3 Defining a Relation Schema in SQL
   2.3.1 Relations in SQL
   2.3.2 Data Types
   2.3.3 Simple Table Declarations
   2.3.4 Modifying Relation Schemas
   2.3.5 Default Values
   2.3.6 Declaring Keys
   2.3.7 Exercises for Section 2.3
   2.4 An Algebraic Query Language
   2.4.1 Why Do We Need a Special Query Language?
   2.4.2 What is an Algebra?
   2.4.3 Overview of Relational Algebra
   2.4.4 Set Operations on Relations
   2.4.5 Projection
   2.4.6 Selection
   2.4.7 Cartesian Product
   2.4.8 Natural Joins
   2.4.9 Theta-Joins
   2.4.10 Combining Operations to Form Queries
   2.4.11 Naming and Renaming
   2.4.12 Relationships Among Operations
   2.4.13 A Linear Notation for Algebraic Expressions
   2.4.14 Exercises for Section 2.4
   2.5 Constraints on Relations
   2.5.1 Relational Algebra as a Constraint Language
   2.5.2 Referential Integrity Constraints
   2.5.3 Key Constraints
   2.5.4 Additional Constraint Examples
   2.5.5 Exercises for Section 2.5
   2.6 Summary of Chapter 2
   2.7 References for Chapter 2

3 Design Theory for Relational Databases
   3.1 Functional Dependencies
   3.1.1 Definition of Functional Dependency
   3.1.2 Keys of Relations
   3.1.3 Superkeys
   3.1.4 Exercises for Section 3.1
   3.2 Rules About Functional Dependencies
   3.2.1 Reasoning About Functional Dependencies
   3.2.2 The Splitting/Combining Rule
   3.2.3 Trivial Functional Dependencies
   3.2.4 Computing the Closure of Attributes
   3.2.5 Why the Closure Algorithm Works
   3.2.6 The Transitive Rule
   3.2.7 Closing Sets of Functional Dependencies
   3.2.8 Projecting Functional Dependencies
   3.2.9 Exercises for Section 3.2
   3.3 Design of Relational Database Schemas
   3.3.1 Anomalies
   3.3.2 Decomposing Relations
   3.3.3 Boyce-Codd Normal Form
   3.3.4 Decomposition into BCN
   3.3.5 Exercises for Section 3.
   3.4 Decomposition: The Good, Bad, and Ugly
   3.4.1 Recovering Information from a Decomposition
   3.4.2 The Chase Test for Lossless Join
   3.4.3 Why the Chase Works
   3.4.4 Dependency Preservation
   3.4.5 Exercises for Section 3.4
   3.5 Third Normal Form
   3.5.1 Definition of Third Normal Form
   3.5.2 The Synthesis Algorithm for 3NF Schemas
   3.5.3 Why the 3NF Synthesis Algorithm Work
   3.5.4 Exercises for Section 3.5
   3.6 Multivalued Dependencies
   3.6.1 Attribute Independence and Its Consequent Redundancy
   3.6.2 Definition of Multivalued Dependencies
   3.6.3 Reasoning About Multivalued Dependencies
   3.6.4 Fourth Normal Form
   3.6.5 Decomposition into Fourth Normal Form
   3.6.6 Relationships Among Normal Forms
   3.6.7 Exercises for Section 3.6
   3.7 An Algorithm for Discovering MVD's
   3.7.1 The Closure and the Chase
   3.7.2 Extending the Chase to MVD's
   3.7.3 Why the Chase Works for MVD's
   3.7.4 Projecting MVD's
   3.7.5 Exercises for Section 3.7
   3.8 Summary of Chapter 3
   3.9 References for Chapter 3

4 High-Level Database Models
   4.1 The Entity/Relationship Model
   4.1.1 Entity Sets
   4.1.2 Attributes
   4.1.3 Relationships
   4.1.4 Entity-Relationship Diagrams
   4.1.5 Instances of an E/R Diagram
   4.1.6 Multiplicity of Binary E/R Relationships
   4.1.7 Multiway Relationships
   4.1.8 Roles in Relationships
   4.1.9 Attributes on Relationships
   4.1.10 Converting Multiway Relationships to Binary
   4.1.11 Subclasses in the E/R Model
   4.1.12 Exercises for Section 4.1
   4.2 Design Principles
   4.2.1 Faithfulness
   4.2.2 Avoiding Redundancy
   4.2.3 Simplicity Counts
   4.2.4 Choosing the Right Relationships
   4.2.5 Picking the Right Kind of Element
   4.2.6 Exercises for Section 4.2
   4.3 Constraints in the E/R Model
   4.3.1 Keys in the E/R Model
   4.3.2 Representing Keys in the E/R Model
   4.3.3 Referential Integrity
   4.3.4 Degree Constraints
   4.3.5 Exercises for Section 4.3
   4.4 Weak Entity Sets
   4.4.1 Causes of Weak Entity Sets
   4.4.2 Requirements for Weak Entity Sets
   4.4.3 Weak Entity Set Notation
   4.4.4 Exercises for Section 4.4
   4.5 From E/R Diagrams to Relational Designs
   4.5.1 From Entity Sets to Relations
   4.5.2 From E/R Relationships to Relations
   4.5.3 Combining Relations
   4.5.4 Handling Weak Entity Sets
   4.5.5 Exercises for Section 4.5
   4.6 Converting Subclass Structures to Relations
   4.6.1 E/R-Style Conversion
   4.6.2 An Object-Oriented Approach
   4.6.3 Using Null Values to Combine Relations
   4.6.4 Comparison of Approaches
   4.6.5 Exercises for Section 4.6
   4.7 Unified Modeling Language
   4.7.1 UML Classes
   4.7.2 Keys for UML classes
   4.7.3 Associations
   4.7.4 Self-Associations
   4.7.5 Association Classes
   4.7.6 Subclasses in UML
   4.7.7 Aggregations and Compositions
   4.7.8 Exercises for Section 4.7
   4.8 From UML Diagrams to Relations
   4.8.1 UML-to-Relations Basics
   4.8.2 From UML Subclasses to Relations
   4.8.3 From Aggregations and Compositions to Relations
   4.8.4 The UML Analog of Weak Entity Sets
   4.8.5 Exercises for Section 4.8
   4.9 Object Definition Language
   4.9.1 Class Declarations
   4.9.2 Attributes in ODL
   4.9.3 Relationships in ODL
   4.9.4 Inverse Relationships
   4.9.5 Multiplicity of Relationships
   4.9.6 Types in ODL
   4.9.7 Subclasses in ODL
   4.9.8 Declaring Keys in ODL
   4.9.9 Exercises for Section 4.9
   4.10 From ODL Designs to Relational Designs
   4.10.1 From ODL Classes to Relations
   4.10.2 Complex Attributes in Classes
   4.10.3 Representing Set-Valued Attributes
   4.10.4 Representing Other Type Constructors
   4.10.5 Representing ODL Relationships
   4.10.6 Exercises for Section 4.10
   4.11 Summary of Chapter 4
   4.12 References for Chapter 4

PART II: Relational Database Programming
5 Algebraic and Logical Query Languages
   5.1 Relational Operations on Bags
   5.1.1 Why Bags?
   5.1.2 Union, Intersection, and Difference of Bags
   5.1.3 Projection of Bags
   5.1.4 Selection on Bags
   5.1.5 Product of Bags
   5.1.6 Joins of Bags
   5.1.7 Exercises for Section 5.1
   5.2 Extended Operators of Relational Algebra
   5.2.1 Duplicate Elimination
   5.2.2 Aggregation Operators
   5.2.3 Grouping
   5.2.4 The Grouping Operator
   5.2.5 Extending the Projection Operator
   5.2.6 The Sorting Operator
   5.2.7 Outerjoins
   5.2.8 Exercises for Section 5.2
   5.3 A Logic for Relations
   5.3.1 Predicates and Atoms
   5.3.2 Arithmetic Atoms
   5.3.3 Datalog Rules and Queries
   5.3.4 Meaning of Datalog Rules
   5.3.5 Extensional and Intensional Predicates
   5.3.6 Datalog Rules Applied to Bags
   5.3.7 Exercises for Section 5.3
   5.4 Relational Algebra and Datalog
   5.4.1 Boolean Operations
   5.4.2 Projection
   5.4.3 Selection
   5.4.4 Product
   5.4.5 Joins
   5.4.6 Simulating Multiple Operations with Datalog
   5.4.7 Comparison Between Datalog and Relational Algebra
   5.4.8 Exercises for Section 5.4
   5.5 Summary of Chapter 5
   5.6 References for Chapter 5

6 The Database Language SQL
   6.1 Simple Queries in SQL
   6.1.1 Projection in SQL
   6.1.2 Selection in SQL
   6.1.3 Comparison of Strings
   6.1.4 Pattern Matching in SQL
   6.1.5 Dates and Times
   6.1.6 Null Values and Comparisons Involving {\tt NULL}
   6.1.7 The Truth-Value {\tt UNKNOWN}
   6.1.8 Ordering the Output
   6.1.9 Exercises for Section 6.1
   6.2 Queries Involving More Than One Relation
   6.2.1 Products and Joins in SQL
   6.2.2 Disambiguating Attributes
   6.2.3 Tuple Variables
   6.2.4 Interpreting Multirelation Queries
   6.2.5 Union, Intersection, and Difference of Queries
   6.2.6 Exercises for Section 6.2
   6.3 Subqueries
   6.3.1 Subqueries that Produce Scalar Values
   6.3.2 Conditions Involving Relations
   6.3.3 Conditions Involving Tuples
   6.3.4 Correlated Subqueries
   6.3.5 Subqueries in {\tt FROM}\ Clauses
   6.3.6 SQL Join Expressions
   6.3.7 Natural Joins
   6.3.8 Outerjoins
   6.3.9 Exercises for Section 6.3
   6.4 Full-Relation Operations
   6.4.1 Eliminating Duplicates
   6.4.2 Duplicates in Unions, Intersections, and Differences
   6.4.3 Grouping and Aggregation in SQL
   6.4.4 Aggregation Operators
   6.4.5 Grouping
   6.4.6 Grouping, Aggregation, and Nulls
   6.4.7 {\tt HAVING} Clauses
   6.4.8 Exercises for Section 6.4
   6.5 Database Modifications
   6.5.1 Insertion
   6.5.2 Deletion
   6.5.3 Updates
   6.5.4 Exercises for Section 6.5
   6.6 Transactions in SQL
   6.6.1 Serializability
   6.6.2 Atomicity
   6.6.3 Transactions
   6.6.4 Read-Only Transactions
   6.6.5 Dirty Reads
   6.6.6 Other Isolation Levels
   6.6.7 Exercises for Section 6.6
   6.7 Summary of Chapter 6
   6.8 References for Chapter 6

7 Constraints and Triggers
   7.1 Keys and Foreign Keys
   7.1.1 Declaring Foreign-Key Constraints
   7.1.2 Maintaining Referential Integrity
   7.1.3 Deferred Checking of Constraints
   7.1.4 Exercises for Section 7.1
   7.2 Constraints on Attributes and Tuples
   7.2.1 Not-Null Constraints
   7.2.2 Attribute-Based {\tt CHECK} Constraints
   7.2.3 Tuple-Based {\tt CHECK} Constraints
   7.2.4 Comparison of Tuple- and Attribute-Based Constraints
   7.2.5 Exercises for Section 7.2
   7.3 Modification of Constraints
   7.3.1 Giving Names to Constraints
   7.3.2 Altering Constraints on Tables
   7.3.3 Exercises for Section 7.3
   7.4 Assertions
   7.4.1 Creating Assertions
   7.4.2 Using Assertions
   7.4.3 Exercises for Section 7.4
   7.5 Triggers
   7.5.1 Triggers in SQL
   7.5.2 The Options for Trigger Design
   7.5.3 Exercises for Section 7.5
   7.6 Summary of Chapter 7
   7.7 References for Chapter 7

8 Views and Indexes
   8.1 Virtual Views
   8.1.1 Declaring Views
   8.1.2 Querying Views
   8.1.3 Renaming Attributes
   8.1.4 Exercises for Section 8.1
   8.2 Modifying Views
   8.2.1 View Removal
   8.2.2 Updatable Views
   8.2.3 Instead-Of Triggers on Views
   8.2.4 Exercises for Section 8.2
   8.3 Indexes in SQL
   8.3.1 Motivation for Indexes
   8.3.2 Declaring Indexes
   8.3.3 Exercises for Section 8.3
   8.4 Selection of Indexes
   8.4.1 A Simple Cost Model
   8.4.2 Some Useful Indexes
   8.4.3 Calculating the Best Indexes to Create
   8.4.4 Automatic Selection of Indexes to Create
   8.4.5 Exercises for Section 8.4
   8.5 Materialized Views
   8.5.1 Maintaining a Materialized View
   8.5.2 Periodic Maintenance of Materialized Views
   8.5.3 Rewriting Queries to Use Materialized Views
   8.5.4 Automatic Creation of Materialized Views
   8.5.5 Exercises for Section 8.5
   8.6 Summary of Chapter 8
   8.7 References for Chapter 8

9 SQL in a Server Environment
   9.1 The Three-Tier Architecture
   9.1.1 The Web-Server Tier
   9.1.2 The Application Tier
   9.1.3 The Database Tier
   9.2 The SQL Environment
   9.2.1 Environments
   9.2.2 Schemas
   9.2.3 Catalogs
   9.2.4 Clients and Servers in the SQL Environment
   9.2.5 Connections
   9.2.6 Sessions
   9.2.7 Modules
   9.3 The SQL/Host-Language Interface
   9.3.1 The Impedance Mismatch Problem
   9.3.2 Connecting SQL to the Host Language
   9.3.3 The {\tt DECLARE} Section
   9.3.4 Using Shared Variables
   9.3.5 Single-Row Select Statements
   9.3.6 Cursors
   9.3.7 Modifications by Cursor
   9.3.8 Protecting Against Concurrent Updates
   9.3.9 Dynamic SQL
   9.3.10 Exercises for Section 9.3
   9.4 Stored Procedures
   9.4.1 Creating PSM Functions and Procedures
   9.4.2 Some Simple Statement Forms in PSM
   9.4.3 Branching Statements
   9.4.4 Queries in PSM
   9.4.5 Loops in PSM
   9.4.6 For-Loops
   9.4.7 Exceptions in PSM
   9.4.8 Using PSM Functions and Procedures
   9.4.9 Exercises for Section 9.4
   9.5 Using a Call-Level Interface
   9.5.1 Introduction to SQL/CLI
   9.5.2 Processing Statements
   9.5.3 Fetching Data From a Query Result
   9.5.4 Passing Parameters to Queries
   9.5.5 Exercises for Section 9.5
   9.6 JDBC
   9.6.1 Introduction to JDBC
   9.6.2 Creating Statements in JDBC
   9.6.3 Cursor Operations in JDBC
   9.6.4 Parameter Passing
   9.6.5 Exercises for Section 9.6
   9.7 PHP
   9.7.1 PHP Basics
   9.7.2 Arrays
   9.7.3 The PEAR DB Library
   9.7.4 Creating a Database Connection Using DB
   9.7.5 Executing SQL Statements
   9.7.6 Cursor Operations in PHP
   9.7.7 Dynamic SQL in PHP
   9.7.8 Exercises for Section 9.7
   9.8 Summary of Chapter 9
   9.9 References for Chapter 9

10 Advanced Topics in Relational Databases
   10.1 Security and User Authorization in SQL
   10.1.1 Privileges
   10.1.2 Creating Privileges
   10.1.3 The Privilege-Checking Process
   10.1.4 Granting Privileges
   10.1.5 Grant Diagrams
   10.1.6 Revoking Privileges
   10.1.7 Exercises for Section 10.1
   10.2 Recursion in SQL
   10.2.1 Defining Recursive Relations in SQL
   10.2.2 Problematic Expressions in Recursive SQL
   10.2.3 Exercises for Section 10.2
   10.3 The Object-Relational Model
   10.3.1 From Relations to Object-Relations
   10.3.2 Nested Relations
   10.3.3 References
   10.3.4 Object-Oriented Versus Object-Relational
   10.3.5 Exercises for Section 10.3
   10.4 User-Defined Types in SQL
   10.4.1 Defining Types in SQL
   10.4.2 Method Declarations in UDT's
   10.4.3 Method Definitions
   10.4.4 Declaring Relations with a UDT
   10.4.5 References
   10.4.6 Creating Object ID's for Tables
   10.4.7 Exercises for Section 10.4
   10.5 Operations on Object-Relational Data
   10.5.1 Following References
   10.5.2 Accessing Components of Tuples with a UDT
   10.5.3 Generator and Mutator Functions
   10.5.4 Ordering Relationships on UDT's
   10.5.5 Exercises for Section 10.5
   10.6 On-Line Analytic Processing
   10.6.1 OLAP and Data Warehouses
   10.6.2 OLAP Applications
   10.6.3 A Multidimensional View of OLAP Data
   10.6.4 Star Schemas
   10.6.5 Slicing and Dicing
   10.6.6 Exercises for Section 10.6
   10.7 Data Cubes
   10.7.1 The Cube Operator
   10.7.2 The Cube Operator in SQL
   10.7.3 Exercises for Section 10.7
   10.8 Summary of Chapter 10
   10.9 References for Chapter 10

PART III: Modeling and Programming for Semistructured Data
11 The Semistructured-Data Model
   11.1 Semistructured Data
   11.1.1 Motivation for the Semistructured-Data Model
   11.1.2 Semistructured Data Representation
   11.1.3 Information Integration Via Semistructured Data
   11.1.4 Exercises for Section 11.1
   11.2 XML
   11.2.1 Semantic Tags
   11.2.2 XML With and Without a Schema
   11.2.3 Well-Formed XML
   11.2.4 Attributes
   11.2.5 Attributes That Connect Elements
   11.2.6 Namespaces
   11.2.7 XML and Databases
   11.2.8 Exercises for Section 11.2
   11.3 Document Type Definitions
   11.3.1 The Form of a DTD
   11.3.2 Using a DTD
   11.3.3 Attribute Lists
   11.3.4 Identifiers and References
   11.3.5 Exercises for Section 11.3
   11.4 XML Schema
   11.4.1 The Form of an XML Schema
   11.4.2 Elements
   11.4.3 Complex Types
   11.4.4 Attributes
   11.4.5 Restricted Simple Types
   11.4.6 Keys in XML Schema
   11.4.7 Foreign Keys in XML Schema
   11.4.8 Exercises for Section 11.4
   11.5 Summary of Chapter 11
   11.6 References for Chapter 11

12 Programming Languages for XML
   12.1 XPath
   12.1.1 The XPath Data Model
   12.1.2 Document Nodes
   12.1.3 Path Expressions
   12.1.4 Relative Path Expressions
   12.1.5 Attributes in Path Expressions
   12.1.6 Axes
   12.1.7 Context of Expressions
   12.1.8 Wildcards
   12.1.9 Conditions in Path Expressions
   12.1.10 Exercises for Section 12.1
   12.2 XQuery
   12.2.1 XQuery Basics
   12.2.2 FLWR Expressions
   12.2.3 Replacement of Variables by Their Values
   12.2.4 Joins in XQuery
   12.2.5 XQuery Comparison Operators
   12.2.6 Elimination of Duplicates
   12.2.7 Quantification in XQuery
   12.2.8 Aggregations
   12.2.9 Branching in XQuery Expressions
   12.2.10 Ordering the Result of a Query
   12.2.11 Exercises for Section 12.2
   12.3 Extensible Stylesheet Language
   12.3.1 XSLT Basics
   12.3.2 Templates
   12.3.3 Obtaining Values From XML Data
   12.3.4 Recursive Use of Templates
   12.3.5 Iteration in XSLT
   12.3.6 Conditionals in XSLT
   12.3.7 Exercises for Section 12.3
   12.4 Summary of Chapter 12
   12.5 References for Chapter 12

PART IV: Database System Implementation
13 Secondary Storage Management
   13.1 The Memory Hierarchy
   13.1.1 The Memory Hierarchy
   13.1.2 Transfer of Data Between Levels
   13.1.3 Volatile and Nonvolatile Storage
   13.1.4 Virtual Memory
   13.1.5 Exercises for Section 13.1
   13.2 Disks
   13.2.1 Mechanics of Disks
   13.2.2 The Disk Controller
   13.2.3 Disk Access Characteristics
   13.2.4 Exercises for Section 13.2
   13.3 Accelerating Access to Secondary Storage
   13.3.1 The I/O Model of Computation
   13.3.2 Organizing Data by Cylinders
   13.3.3 Using Multiple Disks
   13.3.4 Mirroring Disks
   13.3.5 Disk Scheduling and the Elevator Algorithm
   13.3.6 Prefetching and Large-Scale Buffering
   13.3.7 Exercises for Section 13.3
   13.4 Disk Failures
   13.4.1 Intermittent Failures
   13.4.2 Checksums
   13.4.3 Stable Storage
   13.4.4 Error-Handling Capabilities of Stable Storage
   13.4.5 Recovery from Disk Crashes
   13.4.6 Mirroring as a Redundancy Technique
   13.4.7 Parity Blocks
   13.4.8 An Improvement: RAID 5
   13.4.9 Coping With Multiple Disk Crashes
   13.4.10 Exercises for Section 13.4
   13.5 Arranging Data on Disk
   13.5.1 Fixed-Length Records
   13.5.2 Packing Fixed-Length Records into Blocks
   13.5.3 Exercises for Section 13.5
   13.6 Representing Block and Record Addresses
   13.6.1 Addresses in Client-Server Systems
   13.6.2 Logical and Structured Addresses
   13.6.3 Pointer Swizzling
   13.6.4 Returning Blocks to Disk
   13.6.5 Pinned Records and Blocks
   13.6.6 Exercises for Section 13.6
   13.7 Variable-Length Data and Records
   13.7.1 Records With Variable-Length Fields
   13.7.2 Records With Repeating Fields
   13.7.3 Variable-Format Records
   13.7.4 Records That Do Not Fit in a Block
   13.7.5 BLOBs
   13.7.6 Column Stores
   13.7.7 Exercises for Section 13.7
   13.8 Record Modifications
   13.8.1 Insertion
   13.8.2 Deletion
   13.8.3 Update
   13.8.4 Exercises for Section 13.8
   13.9 Summary of Chapter 13
   13.10 References for Chapter 13

14 Index Structures
   14.1 Index-Structure Basics
   14.1.1 Sequential Files
   14.1.2 Dense Indexes
   14.1.3 Sparse Indexes
   14.1.4 Multiple Levels of Index
   14.1.5 Secondary Indexes
   14.1.6 Applications of Secondary Indexes
   14.1.7 Indirection in Secondary Indexes
   14.1.8 Document Retrieval and Inverted Indexes
   14.1.9 Exercises for Section 14.1
   14.2 B-Trees
   14.2.1 The Structure of B-trees
   14.2.2 Applications of B-trees
   14.2.3 Lookup in B-Trees
   14.2.4 Range Queries
   14.2.5 Insertion Into B-Trees
   14.2.6 Deletion From B-Trees
   14.2.7 Efficiency of B-Trees
   14.2.8 Exercises for Section 14.2
   14.3 Hash Tables
   14.3.1 Secondary-Storage Hash Tables
   14.3.2 Insertion Into a Hash Table
   14.3.3 Hash-Table Deletion
   14.3.4 Efficiency of Hash Table Indexes
   14.3.5 Extensible Hash Tables
   14.3.6 Insertion Into Extensible Hash Tables
   14.3.7 Linear Hash Tables
   14.3.8 Insertion Into Linear Hash Tables
   14.3.9 Exercises for Section 14.3
   14.4 Multidimensional Indexes
   14.4.1 Applications of Multidimensional Indexes
   14.4.2 Executing Range Queries Using Conventional Indexes
   14.4.3 Executing Nearest-Neighbor Queries Using Conventional Indexes
   14.4.4 Overview of Multidimensional Index Structures
   14.5 Hash Structures for Multidimensional Data
   14.5.1 Grid Files
   14.5.2 Lookup in a Grid File
   14.5.3 Insertion Into Grid Files
   14.5.4 Performance of Grid Files
   14.5.5 Partitioned Hash Functions
   14.5.6 Comparison of Grid Files and Partitioned Hashing
   14.5.7 Exercises for Section 14.5
   14.6 Tree Structures for Multidimensional Data
   14.6.1 Multiple-Key Indexes
   14.6.2 Performance of Multiple-Key Indexes
   14.6.3 $kd$-Trees
   14.6.4 Operations on $kd$-Trees
   14.6.5 Adapting $kd$-Trees to Secondary Storage
   14.6.6 Quad Trees
   14.6.7 R-Trees
   14.6.8 Operations on R-Trees
   14.6.9 Exercises for Section 14.6
   14.7 Bitmap Indexes
   14.7.1 Motivation for Bitmap Indexes
   14.7.2 Compressed Bitmaps
   14.7.3 Operating on Run-Length-Encoded Bit-Vectors
   14.7.4 Managing Bitmap Indexes
   14.7.5 Exercises for Section 14.7
   14.8 Summary of Chapter 14
   14.9 References for Chapter 14

15 Query Execution
   15.1 Introduction to Physical-Query-Plan Operators
   15.1.1 Scanning Tables
   15.1.2 Sorting While Scanning Tables
   15.1.3 The Computation Model for Physical Operators
   15.1.4 Parameters for Measuring Costs
   15.1.5 I/O Cost for Scan Operators
   15.1.6 Iterators for Implementation of Physical Operators
   15.2 One-Pass Algorithms
   15.2.1 One-Pass Algorithms for Tuple-at-a-Time Operations
   15.2.2 One-Pass Algorithms for Unary, Full-Relation Operations
   15.2.3 One-Pass Algorithms for Binary Operations
   15.2.4 Exercises for Section 15.2
   15.3 Nested-Loop Joins
   15.3.1 Tuple-Based Nested-Loop Join
   15.3.2 An Iterator for Tuple-Based Nested-Loop Join
   15.3.3 Block-Based Nested-Loop Join Algorithm
   15.3.4 Analysis of Nested-Loop Join
   15.3.5 Summary of Algorithms so Far
   15.3.6 Exercises for Section 15.3
   15.4 Two-Pass Algorithms Based on Sorting
   15.4.1 Two-Phase, Multiway Merge-Sort
   15.4.2 Duplicate Elimination Using Sorting
   15.4.3 Grouping and Aggregation Using Sorting
   15.4.4 A Sort-Based Union Algorithm
   15.4.5 Sort-Based Intersection and Difference
   15.4.6 A Simple Sort-Based Join Algorithm
   15.4.7 Analysis of Simple Sort-Join
   15.4.8 A More Efficient Sort-Based Join
   15.4.9 Summary of Sort-Based Algorithms
   15.4.10 Exercises for Section 15.4
   15.5 Two-Pass Algorithms Based on Hashing
   15.5.1 Partitioning Relations by Hashing
   15.5.2 A Hash-Based Algorithm for Duplicate Elimination
   15.5.3 Hash-Based Grouping and Aggregation
   15.5.4 Hash-Based Union, Intersection, and Difference
   15.5.5 The Hash-Join Algorithm
   15.5.6 Saving Some Disk I/O's
   15.5.7 Summary of Hash-Based Algorithms
   15.5.8 Exercises for Section 15.5
   15.6 Index-Based Algorithms
   15.6.1 Clustering and Nonclustering Indexes
   15.6.2 Index-Based Selection
   15.6.3 Joining by Using an Index
   15.6.4 Joins Using a Sorted Index
   15.6.5 Exercises for Section 15.6
   15.7 Buffer Management
   15.7.1 Buffer Management Architecture
   15.7.2 Buffer Management Strategies
   15.7.3 The Relationship Between Physical Operator Selection and Buffer Management
   15.7.4 Exercises for Section 15.7
   15.8 Algorithms Using More Than Two Passes
   15.8.1 Multipass Sort-Based Algorithms
   15.8.2 Performance of Multipass, Sort-Based Algorithms
   15.8.3 Multipass Hash-Based Algorithms
   15.8.4 Performance of Multipass Hash-Based Algorithms
   15.8.5 Exercises for Section 15.8
   15.9 Summary of Chapter 15
   15.10 References for Chapter 15

16 The Query Compiler
   16.1 Parsing and Preprocessing
   16.1.1 Syntax Analysis and Parse Trees
   16.1.2 A Grammar for a Simple Subset of SQL
   16.1.3 The Preprocessor
   16.1.4 Preprocessing Queries Involving Views
   16.1.5 Exercises for Section 16.1
   16.2 Algebraic Laws for Improving Query Plans
   16.2.1 Commutative and Associative Laws
   16.2.2 Laws Involving Selection
   16.2.3 Pushing Selections
   16.2.4 Laws Involving Projection
   16.2.5 Laws About Joins and Products
   16.2.6 Laws Involving Duplicate Elimination
   16.2.7 Laws Involving Grouping and Aggregation
   16.2.8 Exercises for Section 16.2
   16.3 From Parse Trees to Logical Query Plans
   16.3.1 Conversion to Relational Algebra
   16.3.2 Removing Subqueries From Conditions
   16.3.3 Improving the Logical Query Plan
   16.3.4 Grouping Associative/Commutative Operators
   16.3.5 Exercises for Section 16.3
   16.4 Estimating the Cost of Operations
   16.4.1 Estimating Sizes of Intermediate Relations
   16.4.2 Estimating the Size of a Projection
   16.4.3 Estimating the Size of a Selection
   16.4.4 Estimating the Size of a Join
   16.4.5 Natural Joins With Multiple Join Attributes
   16.4.6 Joins of Many Relations
   16.4.7 Estimating Sizes for Other Operations
   16.4.8 Exercises for Section 16.4
   16.5 Introduction to Cost-Based Plan Selection
   16.5.1 Obtaining Estimates for Size Parameters
   16.5.2 Computation of Statistics
   16.5.3 Heuristics for Reducing the Cost of Logical Query Plans
   16.5.4 Approaches to Enumerating Physical Plans
   16.5.5 Exercises for Section 16.5
   16.6 Choosing an Order for Joins
   16.6.1 Significance of Left and Right Join Arguments
   16.6.2 Join Trees
   16.6.3 Left-Deep Join Trees
   16.6.4 Dynamic Programming to Select a Join Order and Grouping
   16.6.5 Dynamic Programming With More Detailed Cost Functions
   16.6.6 A Greedy Algorithm for Selecting a Join Order
   16.6.7 Exercises for Section 16.6
   16.7 Completing the Physical-Query-Plan
   16.7.1 Choosing a Selection Method
   16.7.2 Choosing a Join Method
   16.7.3 Pipelining Versus Materialization
   16.7.4 Pipelining Unary Operations
   16.7.5 Pipelining Binary Operations
   16.7.6 Notation for Physical Query Plans
   16.7.7 Ordering of Physical Operations
   16.7.8 Exercises for Section 16.7
   16.8 Summary of Chapter 16
   16.9 References for Chapter 16

17 Coping With System Failures
   17.1 Issues and Models for Resilient Operation
   17.1.1 Failure Modes
   17.1.2 More About Transactions
   17.1.3 Correct Execution of Transactions
   17.1.4 The Primitive Operations of Transactions
   17.1.5 Exercises for Section 17.1
   17.2 Undo Logging
   17.2.1 Log Records
   17.2.2 The Undo-Logging Rules
   17.2.3 Recovery Using Undo Logging
   17.2.4 Checkpointing
   17.2.5 Nonquiescent Checkpointing
   17.2.6 Exercises for Section 17.2
   17.3 Redo Logging
   17.3.1 The Redo-Logging Rule
   17.3.2 Recovery With Redo Logging
   17.3.3 Checkpointing a Redo Log
   17.3.4 Recovery With a Checkpointed Redo Log
   17.3.5 Exercises for Section 17.3
   17.4 Undo/Redo Logging
   17.4.1 The Undo/Redo Rules
   17.4.2 Recovery With Undo/Redo Logging
   17.4.3 Checkpointing an Undo/Redo Log
   17.4.4 Exercises for Section 17.4
   17.5 Protecting Against Media Failures
   17.5.1 The Archive
   17.5.2 Nonquiescent Archiving
   17.5.3 Recovery Using an Archive and Log
   17.5.4 Exercises for Section 17.5
   17.6 Summary of Chapter 17
   17.7 References for Chapter 17

18 Concurrency Control
   18.1 Serial and Serializable Schedules
   18.1.1 Schedules
   18.1.2 Serial Schedules
   18.1.3 Serializable Schedules
   18.1.4 The Effect of Transaction Semantics
   18.1.5 A Notation for Transactions and Schedules
   18.1.6 Exercises for Section 18.1
   18.2 Conflict-Serializability
   18.2.1 Conflicts
   18.2.2 Precedence Graphs and a Test for Conflict-Serializability
   18.2.3 Why the Precedence-Graph Test Works
   18.2.4 Exercises for Section 18.2
   18.3 Enforcing Serializability by Locks
   18.3.1 Locks
   18.3.2 The Locking Scheduler
   18.3.3 Two-Phase Locking
   18.3.4 Why Two-Phase Locking Works
   18.3.5 Exercises for Section 18.3
   18.4 Locking Systems With Several Lock Modes
   18.4.1 Shared and Exclusive Locks
   18.4.2 Compatibility Matrices
   18.4.3 Upgrading Locks
   18.4.4 Update Locks
   18.4.5 Increment Locks
   18.4.6 Exercises for Section 18.4
   18.5 An Architecture for a Locking Scheduler
   18.5.1 A Scheduler That Inserts Lock Actions
   18.5.2 The Lock Table
   18.5.3 Exercises for Section 18.5
   18.6 Hierarchies of Database Elements
   18.6.1 Locks With Multiple Granularity
   18.6.2 Warning Locks
   18.6.3 Phantoms and Handling Insertions Correctly
   18.6.4 Exercises for Section 18.6
   18.7 The Tree Protocol
   18.7.1 Motivation for Tree-Based Locking
   18.7.2 Rules for Access to Tree-Structured Data
   18.7.3 Why the Tree Protocol Works
   18.7.4 Exercises for Section 18.7
   18.8 Concurrency Control by Timestamps
   18.8.1 Timestamps
   18.8.2 Physically Unrealizable Behaviors
   18.8.3 Problems With Dirty Data
   18.8.4 The Rules for Timestamp-Based Scheduling
   18.8.5 Multiversion Timestamps
   18.8.6 Timestamps Versus Locking
   18.8.7 Exercises for Section 18.8
   18.9 Concurrency Control by Validation
   18.9.1 Architecture of a Validation-Based Scheduler
   18.9.2 The Validation Rules
   18.9.3 Comparison of Three Concurrency-Control Mechanisms
   18.9.4 Exercises for Section 18.9
   18.10 Summary of Chapter 18
   18.11 References for Chapter 18

19 More About Transaction Management
   19.1 Serializability and Recoverability
   19.1.1 The Dirty-Data Problem
   19.1.2 Cascading Rollback
   19.1.3 Recoverable Schedules
   19.1.4 Schedules That Avoid Cascading Rollback
   19.1.5 Managing Rollbacks Using Locking
   19.1.6 Group Commit
   19.1.7 Logical Logging
   19.1.8 Recovery From Logical Logs
   19.1.9 Exercises for Section 19.1
   19.2 Deadlocks
   19.2.1 Deadlock Detection by Timeout
   19.2.2 The Waits-For Graph
   19.2.3 Deadlock Prevention by Ordering Elements
   19.2.4 Detecting Deadlocks by Timestamps
   19.2.5 Comparison of Deadlock-Management Methods
   19.2.6 Exercises for Section 19.2
   19.3 Long-Duration Transactions
   19.3.1 Problems of Long Transactions
   19.3.2 Sagas
   19.3.3 Compensating Transactions
   19.3.4 Why Compensating Transactions Work
   19.3.5 Exercises for Section 19.3
   19.4 Summary of Chapter 19
   19.5 References for Chapter 19

20 Parallel and Distributed Databases
   20.1 Parallel Algorithms on Relations
   20.1.1 Models of Parallelism
   20.1.2 Tuple-at-a-Time Operations in Parallel
   20.1.3 Parallel Algorithms for Full-Relation Operations
   20.1.4 Performance of Parallel Algorithms
   20.1.5 Exercises for Section 20.1
   20.2 The Map-Reduce Parallelism Framework
   20.2.1 The Storage Model
   20.2.2 The Map Function
   20.2.3 The Reduce Function
   20.2.4 Exercises for Section 20.2
   20.3 Distributed Databases
   20.3.1 Distribution of Data
   20.3.2 Distributed Transactions
   20.3.3 Data Replication
   20.3.4 Exercises for Section 20.3
   20.4 Distributed Query Processing
   20.4.1 The Distributed Join Problem
   20.4.2 Semijoin Reductions
   20.4.3 Joins of Many Relations
   20.4.4 Acyclic Hypergraphs
   20.4.5 Full Reducers for Acyclic Hypergraphs
   20.4.6 Why the Full-Reducer Algorithm Works
   20.4.7 Exercises for Section 20.4
   20.5 Distributed Commit
   20.5.1 Supporting Distributed Atomicity
   20.5.2 Two-Phase Commit
   20.5.3 Recovery of Distributed Transactions
   20.5.4 Exercises for Section 20.5
   20.6 Distributed Locking
   20.6.1 Centralized Lock Systems
   20.6.2 A Cost Model for Distributed Locking Algorithms
   20.6.3 Locking Replicated Elements
   20.6.4 Primary-Copy Locking
   20.6.5 Global Locks From Local Locks
   20.6.6 Exercises for Section 20.6
   20.7 Peer-to-Peer Distributed Search
   20.7.1 Peer-to-Peer Networks
   20.7.2 The Distributed-Hashing Problem
   20.7.3 Centralized Solutions for Distributed Hashing
   20.7.4 Chord Circles
   20.7.5 Links in Chord Circles
   20.7.6 Search Using Finger Tables
   20.7.7 Adding New Nodes
   20.7.8 When a Peer Leaves the Network
   20.7.9 When a Peer Fails
   20.7.10 Exercises for Section 20.7
   20.8 Summary of Chapter 20
   20.9 References for Chapter 20

PART V: Other Issues in Management of Massive Data
21 Information Integration
   21.1 Introduction to Information Integration
   21.1.1 Why Information Integration?
   21.1.2 The Heterogeneity Problem
   21.2 Modes of Information Integration
   21.2.1 Federated Database Systems
   21.2.2 Data Warehouses
   21.2.3 Mediators
   21.2.4 Exercises for Section 21.2
   21.3 Wrappers in Mediator-Based Systems
   21.3.1 Templates for Query Patterns
   21.3.2 Wrapper Generators
   21.3.3 Filters
   21.3.4 Other Operations at the Wrapper
   21.3.5 Exercises for Section 21.3
   21.4 Capability-Based Optimization
   21.4.1 The Problem of Limited Source Capabilities
   21.4.2 A Notation for Describing Source Capabilities
   21.4.3 Capability-Based Query-Plan Selection
   21.4.4 Adding Cost-Based Optimization
   21.4.5 Exercises for Section 21.4
   21.5 Optimizing Mediator Queries
   21.5.1 Simplified Adornment Notation
   21.5.2 Obtaining Answers for Subgoals
   21.5.3 The Chain Algorithm
   21.5.4 Incorporating Union Views at the Mediator
   21.5.5 Exercises for Section 21.5
   21.6 Local-as-View Mediators
   21.6.1 Motivation for LAV Mediators
   21.6.2 Terminology for LAV Mediation
   21.6.3 Expanding Solutions
   21.6.4 Containment of Conjunctive Queries
   21.6.5 Why the Containment-Mapping Test Works
   21.6.6 Finding Solutions to a Mediator Query
   21.6.7 Why the LMSS Theorem Holds
   21.6.8 Exercises for Section 21.6
   21.7 Entity Resolution
   21.7.1 Deciding Whether Records Represent a Common Entity
   21.7.2 Merging Similar Records
   21.7.3 Useful Properties of Similarity and Merge Functions
   21.7.4 The R-Swoosh Algorithm for ICAR Records
   21.7.5 Why R-Swoosh Works
   21.7.6 Other Approaches to Entity Resolution
   21.7.7 Exercises for Section 21.7
   21.8 Summary of Chapter 21
   21.9 References for Chapter 21

22 Data Mining
   22.1 Frequent-Itemset Mining
   22.1.1 The Market-Basket Model
   22.1.2 Basic Definitions
   22.1.3 Association Rules
   22.1.4 The Computation Model for Frequent Itemsets
   22.1.5 Exercises for Section 22.1
   22.2 Algorithms for Finding Frequent Itemsets
   22.2.1 The Distribution of Frequent Itemsets
   22.2.2 The Naive Algorithm for Finding Frequent Itemsets
   22.2.3 The A-Priori Algorithm
   22.2.4 Implementation of the A-Priori Algorithm
   22.2.5 Making Better Use of Main Memory
   22.2.6 When to Use the PCY Algorithm
   22.2.7 The Multistage Algorithm
   22.2.8 Exercises for Section 22.2
   22.3 Finding Similar Items
   22.3.1 The Jaccard Measure of Similarity
   22.3.2 Applications of Jaccard Similarity
   22.3.3 Minhashing
   22.3.4 Minhashing and Jaccard Distance
   22.3.5 Why Minhashing Works
   22.3.6 Implementing Minhashing
   22.3.7 Exercises for Section 22.3
   22.4 Locality-Sensitive Hashing
   22.4.1 Entity Resolution as an Example of LSH
   22.4.2 Locality-Sensitive Hashing of Signatures
   22.4.3 Combining Minhashing and Locality-Sensitive Hashing
   22.4.4 Exercises for Section 22.4
   22.5 Clustering of Large-Scale Data
   22.5.1 Applications of Clustering
   22.5.2 Distance Measures
   22.5.3 Agglomerative Clustering
   22.5.4 $k$-Means Algorithms
   22.5.5 $k$-Means for Large-Scale Data
   22.5.6 Processing a Memory Load of Points
   22.5.7 Exercises for Section 22.5
   22.6 Summary of Chapter 22
   22.7 References for Chapter 22

23 Database Systems and the Internet
   23.1 The Architecture of a Search Engine
   23.1.1 Components of a Search Engine
   23.1.2 Web Crawlers
   23.1.3 Query Processing in Search Engines
   23.1.4 Ranking Pages
   23.2 PageRank for Identifying Important Pages
   23.2.1 The Intuition Behind PageRank
   23.2.2 Recursive Formulation of PageRank\nobreakspace {}--- First Try
   23.2.3 Spider Traps and Dead Ends
   23.2.4 PageRank Accounting for Spider Traps and Dead Ends
   23.2.5 Exercises for Section 23.2
   23.3 Topic-Specific PageRank
   23.3.1 Teleport Sets
   23.3.2 Calculating A Topic-Specific PageRank
   23.3.3 Link Spam
   23.3.4 Topic-Specific PageRank and Link Spam
   23.3.5 Exercises for Section 23.3
   23.4 Data Streams
   23.4.1 Data-Stream-Management Systems
   23.4.2 Stream Applications
   23.4.3 A Data-Stream Data Model
   23.4.4 Converting Streams Into Relations
   23.4.5 Converting Relations Into Streams
   23.4.6 Exercises for Section 23.4
   23.5 Data Mining of Streams
   23.5.1 Motivation
   23.5.2 Counting Bits
   23.5.3 Counting the Number of Distinct Elements
   23.5.4 Exercises for Section 23.5
   23.6 Summary of Chapter 23
   23.7 References for Chapter 23

1. 資料庫系統的世界
1.1 資料庫系統的演進
1.1.1 早期資料庫管理系統
1.1.2 關聯式資料庫系統
1.1.3 越來越小的系統
1.1.4 越來越大的系統
1.1.5 資訊整合
1.2 資料庫管理系統概述
1.2.1 資料定義語言指令
1.2.2 查詢處理概述
1.2.3 儲存和緩衝管理
1.2.4 交易處理
1.2.5 查詢處理器
1.3 資料庫系統研究大綱
1.4 第一章參考資料

PART I: 關聯式資料庫建模
2. 資料的關聯模型
2.1 資料模型概述
2.1.1 什麼是資料模型？
2.1.2 重要的資料模型
2.1.3 簡介關聯模型
2.1.4 簡介半結構化模型
2.1.5 其他資料模型
2.1.6 建模方法的比較
2.2 關聯模型的基礎
2.2.1 屬性
2.2.2 模式
2.2.3 元組
2.2.4 範圍
2.2.5 關聯的等價表示
2.2.6 關聯實例
2.2.7 關聯的鍵
2.2.8 一個範例資料庫模式
2.2.9 第2.2節的練習
2.3 在SQL中定義關聯模式
2.3.1 SQL中的關聯
2.3.2 資料類型
2.3.3 簡單表聲明
2.3.4 修改關聯模式
2.3.5 預設值
2.3.6 宣告鍵
2.3.7 第2.3節的練習
2.4 代數查詢語言
2.4.1 為什麼需要特殊的查詢語言？
2.4.2 什麼是代數？
2.4.3 關聯代數概述
2.4.4 關聯的集合運算
2.4.5 投影
2.4.6 選擇
2.4.7 笛卡爾積
2.4.8 自然連接
2.4.9 Theta連接
2.4.10 組合操作形成查詢
2.4.11 命名和重新命名
2.4.12 操作之間的關係
2.4.13 代數表達式的線性表示
2.4.14 第2.4節的練習
2.5 關聯的限制
2.5.1 關聯代數作為限制語言
2.5.2 參照完整性限制
2.5.3 鍵限制
2.5.4 附加限制範例
2.5.5 第2.5節的練習
2.6 第2章摘要
2.7 第2章參考資料

3. 關聯資料庫的設計理論
3.1 函數相依性
3.1.1 函數相依性的定義
3.1.2 關聯的鍵
3.1.3 超鍵
3.1.4 第3.1節的練習
3.2 函數相依性的規則