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Database systems the complete book

Author: Hector Garcia-Molina; Jeffrey D Ullman; Jennifer Widom
Publisher: Upper Saddle River, NJ Pearson 2009
Series: Pearson international edition
Edition/Format:   Book : English : 2. ed., international edView all editions and formats
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Genre/Form: 0 Gesamtdarstellung
Document Type: Book
All Authors / Contributors: Hector Garcia-Molina; Jeffrey D Ullman; Jennifer Widom
ISBN: 9780131354289 0131354280 0131873253 9780131873254
OCLC Number: 263455657
Description: XXXVI, 1203 S.
Contents: TABLE OF CONTENTS 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 Models 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 BCNF 3.3.5 Exercises for Section 3.3 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 Works 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
Series Title: Pearson international edition
Responsibility: Hector Garcia-Molina ; Jeffrey D. Ullman ; Jennifer Widom

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