DBMS Overview and Concepts

DBMS History

• The first general-purpose DBMS was designed by Charles William Bachman III at General Electric.
• IMS (Information Management System) was based on the hierarchical data model.

DBMS Advantages

• Data Independence: Applications are shielded from data representation and storage details.
• Data Administration: Centralized data management improves efficiency when multiple users share data.
• Reduced Application Development Time: DBMS supports common functions, speeding up development.

Data and Knowledge

• Data: Raw facts without inherent meaning.
• Knowledge: Can be derived from existing data.
• Data Management: Focuses on generating, storing, and retrieving data.
• End-user Data: Raw facts relevant to end-users.
• Metadata: Data about data used to integrate and manage end-user data.

Database Types

• Workgroup Database: Supports a small number of users (usually under 50), often within a specific department.
• Enterprise Database: Supports a large number of users across many departments.
• Centralized Database: Data is stored at a single location.
• Decentralized Database: Data is distributed across multiple locations (Bitcoin is an example).

Data Structures

• Field: A character or group of characters with a specific meaning, used to define and store data.
• Record: A logically connected set of fields that describes a person, place, or thing.
• File: A collection of related records.

ACID Properties

• Atomicity: Transaction is treated as a single unit, either fully committed or completely rolled back.
• Consistency: Database transitions from one valid state to another.
• Isolation: Transactions are isolated from each other, ensuring their independence.
• Durability: Committed changes are permanent and survive system failures.

File Systems vs. DBMS

• File Systems: Exhibit structural dependence, meaning file access depends on its structure.
• DBMS: Offers structural independence, allowing changes in file structure without affecting application access.
• DBMS: Offers data independence, allowing changes in storage characteristics without affecting application access.

Data Redundancy and Anomaly

• Data Redundancy: The same data is stored unnecessarily at multiple locations.
• Data Anomaly: Occurring when redundant data is not updated consistently, leading to inconsistencies.

Database System Components

• Hardware: Physical devices like storage devices, printers, and network devices.
• DBMS Software: Manages the database within the system.
• Procedures: Instructions and rules governing the database system's design and use.
• Data: Collection of facts stored in the database.
• People: Include all users of the database system.

DBMS Functions

• Data Transformation and Presentation: DBMS converts input data to conform to required data structures.
• Backup and Recovery Management: Recovers the database after failures (e.g., disk errors or power outages).
• Security Management: Enforces user security and data privacy.
• Data Dictionary Management: Stores metadata, including data element definitions and relationships.
• Data Storage Management: Creates and manages complex data storage structures.
• Multi-User Access Control: Allows multiple users to concurrently access the database.
• Performance Tuning: Optimizes database storage and access speed.

Data Models

• Semantic Data Model: Provides a high-level abstract understanding of data, emphasizing meaning.
• Relational Model: The foundation of most modern database systems, based on mathematical relations.
• Hierarchical Model: Developed in the 1960s for managing large amounts of data (e.g., Apollo project).
• Network Model: Created to improve data relationships, performance, and database standardization.

Schema

• Schema: Description of data using a specific data model.
• Conceptual/Logical Schema: A high-level view of the entire database, seen by the database administrator.
• Physical/Internal Schema: The actual physical storage structure and access paths, used for database implementation.
• External Schema: Defines the view of the database for a specific user or group of users.
• Each database has one conceptual schema and one physical schema but may have multiple external schemas.

Entity Relationship (ER) Model

• Entity: Something about which data is collected and stored.
• Relationship: Association between entities.
• Cardinality: The maximum number of relationships an entity can have with another entity.
• Modality: The minimum number of relationships an entity must have with another entity.
• Intersection Entity: Used to resolve many-to-many relationships.
• Independent Entity: Can exist without another entity.
• Dependent Entity: Relies on the existence of another entity.

Object-Oriented Data Modeling

• Class Diagram: A UML diagram that depicts classes and their relationships.
• Generalization: A taxonomic relationship between a more general and a more specific classifier.
• Association: Relationships between classes in a UML Class Diagram.
• Class Diagram: Foundation for component and deployment diagrams.
• Object/Relational DBMSs: Combine the advantages of relational and object-oriented models.
• SQL Data Services: Essential components of relational database vendors' internet services.

Transactions

• Transaction: A single execution of a user program in a DBMS.
• Partial transactions are not allowed; transactions are treated as single units.

Other Key Concepts

• Data Model: A simplified graphical representation of complex real-world data structures.
• UML: Unified Modeling Language, a language based on OO concepts with diagrams and symbols for graphical modeling.
• API (Application Programming Interface): Allows applications to communicate with the operating system or DBMS.

Key Dates

• 1976: Introduction of the ER diagram.
• 1960s: Development of the hierarchical data model.

Generations of Data Models

• 1st Generation: Hierarchical
• 2nd Generation: Network
• 3rd Generation: Relational
• 4th Generation: NoSQL, including key-value store, column store
• 5th Generation: XML, hybrid, object-oriented/object relational