Modern Database Management, Global Edition
Höfundar: Jeff Hoffer, Jeffrey A. Hoffer, Ramesh Venkataraman, Heikki Topi
5.490 kr.
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For courses in database management. A comprehensive text on the latest in database development Focusing on what leading database practitioners say are the most important aspects to database development, Modern Database Management presents sound pedagogy and topics that are critical for the practical success of database professionals. The 13th Edition updates and expands materials in areas undergoing rapid change as a result of improved managerial practices, database design tools and methodologies, and database technology - such as application security, multi-user solutions, and more - to reflect major trends in the field and the skills required of modern information systems graduates.
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Annað
- Höfundar: Jeff Hoffer, Jeffrey A. Hoffer, Ramesh Venkataraman, Heikki Topi
- Útgáfa:13
- Útgáfudagur: 2019-06-17
- Hægt að prenta út 2 bls.
- Hægt að afrita 2 bls.
- Format:Page Fidelity
- ISBN 13: 9781292263410
- Print ISBN: 9781292263359
- ISBN 10: 1292263415
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
- Data Models
- 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
- Systems Development Life Cycle
- 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
- 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
- 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
- Overview of Business Rules
- 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
- Entities
- 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
- Showing Product Information
- Showing Product Line Information
- Showing Customer Order Status
- Showing Product Sales
- 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
- Basic Concepts and Notation
- 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
- A Revised Data Modeling Process with Packaged Data Models
- Packaged Data Model Examples
- Learning Objectives
- Introduction
- The Relational Data Model
- Basic Definitions
- Relational Data Structure
- Relational Keys
- Properties of Relations
- Removing Multivalued Attributes from Tables
- Sample Database
- Basic Definitions
- 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
- Step 1: Map Regular Entities
- Introduction to Normalization
- Steps in Normalization
- Functional Dependencies and Keys
- Determinants
- Candidate Keys
- 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
- An Example
- View Integration Problems
- Synonyms
- Homonyms
- Transitive Dependencies
- Supertype/Subtype Relationships
- 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.
- 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
- Choosing Data Types
- Denormalization
- Opportunities for and Types of Denormalization
- Denormalize with Caution
- Partitioning
- File Organizations
- Heap File Organization
- Sequential File Organizations
- Indexed File Organizations
- Hashed File Organizations
- Clustering Files
- Designing Controls for Files
- Creating a Unique Key Index
- Creating a Secondary (Nonunique) Key Index
- When to Use Indexes
- Parallel Query Processing
- Overriding Automatic Query Optimization
- Data Dictionary
- Repositories
- Views
- Integrity Controls
- Authorization Rules
- User-Defined Procedures
- Encryption
- Authentication Schemes
- Passwords
- Strong Authentication
- 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 Models for Providing Data Management Services 407
- Benefits and Downsides of Using Cloud-Based Management Services 408
- 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
- Status versus Event Data
- Transient versus Periodic Data
- An Example of Transient and Periodic Data
- Transient Data
- Periodic Data
- Other Data Warehouse Changes
- 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
- General Approaches to Data Integration
- Data Federation
- Data Propagation
- Characteristics of Data after ETL
- The ETL Process
- Mapping and Metadata Management
- Extract
- Cleanse
- Load and Index
- Data Transformation Functions
- Record-Level Functions
- Field-Level Functions
- Speed of Processing
- Moving the Data Warehouse into the Cloud
- Dealing with Unstructured Data
- 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
- 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
- Applications of Big Data and Analytics
- 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
- Characteristics of Quality Data
- Data Availability
- Costs of Downtime
- Measures to Ensure Availability
- Hardware Failures
- Loss or Corruption of Data
- Human Error
- Maintenance Downtime
- Network-Related Problems
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- Gerð : 208
- Höfundur : 9714
- Útgáfuár : 2019
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