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File-Based System
Stores data in separate files managed by individual applications.
Data
Raw facts that have not been processed.
Field
Single piece of data, such as a customer name.
Record
Collection of related fields describing a person, place, or thing.
File
Collection of related records.
Data Redundancy
Unnecessary duplication of data in multiple locations.
Data Inconsistency
Different values for the same data across locations.
Data Dependence
Changes in data storage characteristics affect programs.
Data Independence
Data storage characteristics can change without affecting programs.
Structural Dependence
Changing a file's structure requires program changes.
Structural Independence
File structure can change without affecting programs.
File-Based System Limitations
Data redundancy, inconsistency, limited sharing/security, dependence, and lengthy development.
Data Anomaly
Problem caused when changes to redundant data are not completed correctly.
Insertion Anomaly
Cannot add new data without adding unrelated information.
Update Anomaly
Same information must be updated in multiple places.
Deletion Anomaly
Deleting data unintentionally removes other important information.
Database
Shared, integrated computer structure that stores data.
End-User Data
Raw facts that are important to users.
Metadata
Data about data; describes characteristics and relationships.
System Catalog
Stores metadata about the database.
Information
Processed data that provides meaning and context.
Good Information
Accurate, relevant, and timely.
Ubiquitous Data
Data is abundant and everywhere.
Pervasive Data
Data is widespread and persistent.
Single-User Database
Supports one user at a time.
Multiuser Database
Supports multiple users simultaneously.
Workgroup Database
Supports a small group or department.
Enterprise Database
Supports users across many departments.
Centralized Database
Stores data at a single location.
Distributed Database
Stores data across multiple locations.
Cloud Database
Created and maintained using cloud data services.
General-Purpose Database
Stores data across multiple subject areas.
Discipline-Specific Database
Focuses on a particular subject area.
Operational Database
Supports an organization's daily operations.
Data Warehouse
Stores data optimized for analysis and decision-making.
OLAP (Online Analytical Processing)
Analyzes and models data in a data warehouse.
Business Intelligence (BI)
Processes business data to support decision-making.
Structured Data
Data organized into a defined format.
Unstructured Data
Data in its original raw state.
Semistructured Data
Data that has been partially organized.
NoSQL (Not Only SQL)
Handles large amounts of structured and unstructured data.
DBMS
Software that manages database structure, controls access, and connects users to the database.
Database System Components
Hardware, software, people, procedures, and data.
Data Dictionary Management
Stores definitions of data elements and their relationships.
Data Storage Management
Manages data storage and performance.
Security Management
Protects data and controls user access.
Multiuser Access Control
Allows simultaneous access without compromising data integrity.
Backup and Recovery Management
Restores the database after a failure.
Data Integrity Management
Minimizes redundancy and maximizes consistency.
SQL
Structured Query Language; standard language for accessing and querying databases.
DBMS Advantages
Improved data sharing, security, consistency, integration, and decision-making.
DBMS Disadvantages
Higher costs, complexity, maintenance, vendor dependence, and upgrades.
Database Design
Creating a flexible structure to store and manage data.
Generative AI
Uses learned patterns from input data to generate new content.
Machine Learning
Uses statistical techniques to identify patterns and predict trends.
Natural Language Processing (NLP)
AI that processes human language.
AI Challenges
Ethical concerns, data privacy, inherited bias, and resource utilization.