Efnisyfirlit

• Title Page
• Copyright Page
• Brief Contents
• Contents
• Preface
• Acknowledgments
• Preface
• Part I: The Context of Database Management
• An Overview of Part I
• Chapter 1: The Database Environment and Development Process
• Learning Objectives
• Data Matter!
• Introduction
• Basic Concepts and Definitions
• Data
• Data versus Information
• Metadata
• Traditional File Processing Systems
• File Processing Systems at Pine Valley Furniture Company
• Disadvantages of File Processing Systems
• Program-Data Dependence
• Duplication of Data
• Limited Data Sharing
• Lengthy Development Times
• Excessive Program Maintenance
• The Database Approach
• Data Models
• Entities
• Relationships
• Relational Databases
• Database Management Systems
• Advantages of the Database Approach
• Program-Data Independence
• Planned Data Redundancy
• Improved Data Consistency
• Improved Data Sharing
• Increased Productivity of Application Development
• Enforcement of Standards
• Improved Data Quality
• Improved Data Accessibility and Responsiveness
• Reduced Program Maintenance
• Improved Decision Support
• Cautions about Database Benefits
• Costs and Risks of the Database Approach
• New, Specialized Personnel
• Installation and Management Cost and Complexity
• Conversion Costs
• Need for Explicit Backup and Recovery
• Organizational Conflict
• Integrated Data Management Framework
• Components of the Database Environment
• The Database Development Process
• Systems Development Life Cycle
• Planning—Enterprise Modeling
• Planning—Conceptual Data Modeling
• Analysis—Conceptual Data Modeling
• Design—Logical Database Design
• Design—Physical Database Design and Definition
• Implementation—Database Implementation
• Maintenance—Database Maintenance
• Alternative Information Systems Development Approaches
• Three-Schema Architecture for Database Development
• Managing the People Involved in Database Development
• Evolution of Database Systems
• 1960s
• 1970s
• 1980s
• 1990s
• 2000 and Beyond
• The Range of Database Applications
• Personal Databases
• Departmental Multi-Tiered Client/Server Databases
• Enterprise Applications
• Enterprise Systems
• Data Warehouses
• Data Lake
• Developing a Database Application for Pine Valley Furniture Company
• Database Evolution at Pine Valley Furniture Company
• Project Planning
• Analyzing Database Requirements
• Designing the Database
• Using the Database
• Administering the Database
• Future of Databases at Pine Valley
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Case: Forondo Artist Management Excellence Inc.
• Part II: Database Analysis and Logical Design
• An Overview of Part II
• Chapter 2: Modeling Data in the Organization
• Learning Objectives
• Introduction
• The E-R Model: An Overview
• Sample E-R Diagram
• E-R Model Notation
• Modeling the Rules of the Organization
• Overview of Business Rules
• The Business Rules Paradigm
• Scope of Business Rules
• Good Business Rules
• Gathering Business Rules
• Data Names and Definitions
• Data Names
• Data Definitions
• Good Data Definitions
• Modeling Entities and Attributes
• Entities
• Entity Type versus Entity Instance
• Entity Type versus System Input, Output, or User
• Strong versus Weak Entity Types
• Naming and Defining Entity Types
• Attributes
• Required versus Optional Attributes
• Simple versus Composite Attributes
• Single-valued versus Multivalued Attributes
• Stored versus Derived Attributes
• Identifier Attribute
• Naming and Defining Attributes
• Modeling Relationships
• Basic Concepts and Definitions in Relationships
• Attributes on Relationships
• Associative Entities
• Degree of a Relationship
• Unary Relationship
• Binary Relationship
• Ternary Relationship
• Attributes or Entity?
• Cardinality Constraints
• Minimum Cardinality
• Maximum Cardinality
• Some Examples of Relationships and Their Cardinalities
• A Ternary Relationship
• Modeling Time-Dependent Data
• Modeling Multiple Relationships Between Entity Types
• Naming and Defining Relationships
• E-R Modeling Example: Pine Valley Furniture Company
• Database Processing At Pine Valley Furniture
• Showing Product Information
• Showing Product Line Information
• Showing Customer Order Status
• Showing Product Sales
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Case: Forondo Artist Management Excellence Inc.
• Chapter 3: The Enhanced E-R Model
• Learning Objectives
• Introduction
• Representing Supertypes and Subtypes
• Basic Concepts and Notation
• An Example of a Supertype/Subtype Relationship
• Attribute Inheritance
• When to Use Supertype/Subtype Relationships
• Representing Specialization and Generalization
• Generalization
• Specialization
• Combining Specialization and Generalization
• Specifying Constraints in Supertype/Subtype Relationships
• Specifying Completeness Constraints
• Total Specialization Rule
• Partial Specialization Rule
• Specifying Disjointness Constraints
• Disjoint Rule
• Overlap Rule
• Defining Subtype Discriminators
• Disjoint Subtypes
• Overlapping Subtypes
• Defining Supertype/Subtype Hierarchies
• An Example of a Supertype/Subtype Hierarchy
• Summary of Supertype/Subtype Hierarchies
• EER Modeling Example: Pine Valley Furniture Company
• Entity Clustering
• Packaged Data Models
• A Revised Data Modeling Process with Packaged Data Models
• Packaged Data Model Examples
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Case: Forondo Artist Management Excellence Inc.
• Chapter 4: Logical Database Design and the Relational Model
• Learning Objectives
• Introduction
• The Relational Data Model
• Basic Definitions
• Relational Data Structure
• Relational Keys
• Properties of Relations
• Removing Multivalued Attributes from Tables
• Sample Database
• Integrity Constraints
• Domain Constraints
• Entity Integrity
• Referential Integrity
• Creating Relational Tables
• Well-Structured Relations
• Transforming EER Diagrams into Relations
• Step 1: Map Regular Entities
• Composite Attributes
• Multivalued Attributes
• Step 2: Map Weak Entities
• When to Create a Surrogate Key
• Step 3: Map Binary Relationships
• Map Binary One-to-Many Relationships
• Map Binary Many-to-Many Relationships
• Map Binary One-to-One Relationships
• Step 4: Map Associative Entities
• Identifier not Assigned
• Identifier Assigned
• Step 5: Map Unary Relationships
• Unary One-to-Many Relationships
• Unary Many-to-Many Relationships
• Step 6: Map Ternary (and n-ary) Relationships
• Step 7: Map Supertype/Subtype Relationships
• Summary of EER-to-Relational Transformations
• Introduction to Normalization
• Steps in Normalization
• Functional Dependencies and Keys
• Determinants
• Candidate Keys
• Normalization Example: Pine Valley Furniture Company
• Step 0: Represent the View in Tabular Form
• Step 1: Convert to First Normal Form
• Remove Repeating Groups
• Select the Primary Key
• Anomalies in 1NF
• Step 2: Convert to Second Normal Form
• Step 3: Convert to Third Normal Form
• Removing Transitive Dependencies
• Determinants and Normalization
• Step 4: Further Normalization
• Merging Relations
• An Example
• View Integration Problems
• Synonyms
• Homonyms
• Transitive Dependencies
• Supertype/Subtype Relationships
• A Final Step for Defining Relational Keys
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Case: Forondo Artist Management Excellence Inc.
• Part III: Database Implementation and Use
• An Overview of Part III
• Chapter 5: Introduction to SQL
• Learning Objectives
• Introduction
• Origins of the SQL Standard
• The SQL Environment
• SQL Data Types
• Defining A Database in SQL
• Generating SQL Database Definitions
• Creating Tables
• Creating Data Integrity Controls
• Changing Table Definitions
• Removing Tables
• Inserting, Updating, and Deleting Data
• Batch Input
• Deleting Database Contents
• Updating Database Contents
• Internal Schema Definition in RDBMSs
• Creating Indexes
• Processing Single Tables
• Clauses of the SELECT Statement
• Using Expressions
• Using Functions
• Using Wildcards
• Using Comparison Operators
• Using Null Values
• Using Boolean Operators
• Using Ranges for Qualification
• Using Distinct Values
• Using IN and NOT IN with Lists
• Sorting Results: The ORDER BY Clause
• Categorizing Results: The GROUP BY Clause
• Qualifying Results by Categories: The HAVING Clause
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Case: Forondo Artist Management Excellence Inc.
• Chapter 6: Advanced SQL
• Learning Objectives
• Introduction
• Processing Multiple Tables
• Equi-Join
• Natural Join
• Outer Join
• Sample Join Involving Four Tables
• Self-Join
• Subqueries
• Correlated Subqueries
• Using Derived Tables
• Combinings Queries
• Conditional Expressions
• More Complicated SQL Queries
• Tips for Developing Queries
• Guidelines for Better Query Design
• Using and Defining Views
• Materialized Views
• Triggers and Routines
• Triggers
• Routines and Other Programming Extensions
• Example Routine in Oracle’s PL/SQL
• Data Dictionary Facilities
• Recent Enhancements and Extensions to SQL
• Analytical and OLAP Functions
• New Temporal Features in SQL
• Other Enhancements
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Case: Forondo Artist Management Excellence Inc.
• Chapter 7: Databases in Applications
• Learning Objectives
• Location, Location, Location!
• Introduction
• Client/Server Architectures
• Databases in Three-Tier Applications
• A Java Web Application
• A Python Web Application
• Key Considerations in Three-Tier Applications
• Stored Procedures
• Transactions
• Database Connections
• Key Benefits of Three-Tier Applications
• Transaction Integrity
• Controlling Concurrent Access
• The Problem of Lost Updates
• Serializability
• Locking Mechanisms
• Locking Level
• Types of Locks
• Deadlock
• Managing Deadlock
• Versioning
• Managing Data Security in an Application Context
• Threats to Data Security
• Establishing Client/Server Security
• Server Security
• Network Security
• Application Security Issues in Three-Tier Client/Server Environments
• Data Privacy
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Case: Forondo Artist Management Excellence Inc.
• Chapter 8: Physical Database Design and Database Infrastructure
• Learning Objectives
• Introduction
• The Physical Database Design Process
• Who Is Responsible for Physical Database Design?
• Physical Database Design as a Basis for Regulatory Compliance
• SOX and Databases
• IT Change Management
• Logical Access to Data
• IT Operations
• Data Volume and Usage Analysis
• Designing Fields
• Choosing Data Types
• Coding Techniques
• Controlling Data Integrity
• Handling Missing Data
• Denormalizing and Partitioning Data
• Denormalization
• Opportunities for and Types of Denormalization
• Denormalize with Caution
• Partitioning
• Designing Physical Database Files
• File Organizations
• Heap File Organization
• Sequential File Organizations
• Indexed File Organizations
• Hashed File Organizations
• Clustering Files
• Designing Controls for Files
• Using and Selecting Indexes
• Creating a Unique Key Index
• Creating a Secondary (Nonunique) Key Index
• When to Use Indexes
• Designing a Database for Optimal Query Performance
• Parallel Query Processing
• Overriding Automatic Query Optimization
• Data Dictionaries and Repositories
• Data Dictionary
• Repositories
• Database Software Data Security Features
• Views
• Integrity Controls
• Authorization Rules
• User-Defined Procedures
• Encryption
• Authentication Schemes
• Passwords
• Strong Authentication
• Database Backup and Recovery
• Basic Recovery Facilities
• Backup Facilities
• Journalizing Facilities
• Checkpoint Facility
• Recovery Manager
• Recovery and Restart Procedures
• Disk Mirroring
• Restore/Rerun
• Backward Recovery
• Forward Recovery
• Types of Database Failure
• Aborted Transactions
• Incorrect Data
• System Failure
• Database Destruction
• Disaster Recovery
• Cloud-Based Database Infrastructure
• Cloud-Based Models for Providing Data Management Services 407
• Benefits and Downsides of Using Cloud-Based Management Services 408
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Case: Forondo Artist Management Excellence Inc.
• Part IV: Advanced Database Topics
• An Overview of Part IV
• Chapter 9: Data Warehousing and Data Integration
• Learning Objectives
• Introduction
• Basic Concepts of Data Warehousing
• A Brief History of Data Warehousing
• The Need for Data Warehousing
• Need for a Company-Wide View
• Need to Separate Operational and Informational Systems
• Data Warehouse Architectures
• Independent Data Mart Data Warehousing Environment
• Dependent Data Mart and Operational Data Store Architecture: A Three-Level Approach
• Logical Data Mart and Real-Time Data Warehouse Architecture
• Three-Layer Data Architecture
• Role of the Enterprise Data Model
• Role of Metadata
• Some Characteristics of Data Warehouse Data
• Status versus Event Data
• Transient versus Periodic Data
• An Example of Transient and Periodic Data
• Transient Data
• Periodic Data
• Other Data Warehouse Changes
• The Derived Data Layer
• Characteristics of Derived Data
• The Star Schema
• Fact Tables and Dimension Tables
• Example Star Schema
• Surrogate Key
• Grain of the Fact Table
• Duration of the Database
• Size of the Fact Table
• Modeling Date and Time
• Variations of the Star Schema
• Multiple Fact Tables
• Factless Fact Tables
• Normalizing Dimension Tables
• Multivalued Dimensions
• Hierarchies
• Slowly Changing Dimensions
• Determining Dimensions and Facts
• Data Integration: An Overview
• General Approaches to Data Integration
• Data Federation
• Data Propagation
• Data Integration for Data Warehousing: The Reconciled Data Layer
• Characteristics of Data after ETL
• The ETL Process
• Mapping and Metadata Management
• Extract
• Cleanse
• Load and Index
• Data Transformation
• Data Transformation Functions
• Record-Level Functions
• Field-Level Functions
• Data Warehouse Administration
• The Future of Data Warehousing: Integration with Other Forms of Data Management and Analytics
• Speed of Processing
• Moving the Data Warehouse into the Cloud
• Dealing with Unstructured Data
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Chapter 10: Big Data Technologies
• Learning Objectives
• Introduction
• Moving Beyond Transactional and Data Warehousing Databases
• Big Data
• NoSQL
• Classification of NoSQL DBMSs
• Key-Value Stores
• Document Stores
• Wide-Column Stores
• Graph-Oriented Databases
• NoSQL Examples
• Redis
• MongoDB
• Apache Cassandra
• Neo4j
• A NoSQL Example: MongoDB
• Documents
• Collections
• Relationships
• Querying MongoDB
• Impact of NoSQL on Database Professionals
• Hadoop
• Components of Hadoop
• The Hadoop Distributed File System (HDFS)
• MapReduce
• Pig
• Hive
• HBase
• A Practical Introduction to Pig
• Loading Data
• Transforming Data
• A Practical Introduction to Hive
• Creating a Table
• Loading Data into the Table
• Processing the Data
• Integrated Analytics and Data Science Platforms
• HP HAVEn
• Teradata Aster
• IBM Big Data Platform
• Putting It All Together: Integrated Data Architecture
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• References
• Further Reading
• Web Resources
• Chapter 11: Analytics and Its Implications
• Learning Objectives
• Introduction
• Analytics
• Types of Analytics
• Use of Descriptive Analytics
• SQL OLAP Querying
• OLAP Tools
• Data Visualization
• Business Performance Management and Dashboards
• Use of Predictive Analytics
• Data Mining Tools
• Examples of Predictive Analytics
• Use of Prescriptive Analytics
• Key User Tools for Analytics
• Analytical and OLAP Functions
• R 524
• Python
• Apache Spark
• Data Management Infrastructure for Analytics
• Impact of Big Data and Analytics
• Applications of Big Data and Analytics
• Business
• E-Government and Politics
• Science and Technology
• Smart Health and Well-Being
• Security and Public Safety
• Implications of Big Data Analytics and Decision Making
• Personal Privacy versus Collective Benefits
• Ownership and Access
• Quality and Reuse of Data and Algorithms
• Transparency and Validation
• Changing Nature of Work
• Demands for Workforce Capabilities and Education
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• References
• Further Reading
• Chapter 12: Data and Database Administration with Focus on Data Quality
• Learning Objectives
• Introduction
• Overview of Data and Database Administration
• Data Administration
• Database Administration
• Traditional Database Administration
• Trends in Database Administration
• Evolving Data Administration Roles
• The Open Source Movement and Database Management
• Data Governance
• Managing Data Quality
• Characteristics of Quality Data
• External Data Sources
• Redundant Data Storage and Inconsistent Metadata
• Data Entry Problems
• Lack of Organizational Commitment
• Data Quality Improvement
• Get the Business Buy-In
• Conduct a Data Quality Audit
• Establish a Data Stewardship Program
• Improve Data Capture Processes
• Apply Modern Data Management Principles and Technology
• Apply TQM Principles and Practices
• Summary of Data Quality
• Data Availability
• Costs of Downtime
• Measures to Ensure Availability
• Hardware Failures
• Loss or Corruption of Data
• Human Error
• Maintenance Downtime
• Network-Related Problems
• Master Data Management
• Summary
• Key Terms
• Review Questions
• Problems and Exercises
• Field Exercises
• References
• Further Reading
• Web Resources
• Glossary of Acronyms
• Glossary of Terms
• Index