Fundamentals of Database Systems

Overview of Database Systems

• Database Systems store, manage, and retrieve data efficiently.

Data Models

• Definition: Concepts that define the structure of a database, operations for manipulating, and constraints to enforce.
• Structure and Constraints:
  • Use constructs to define structure (elements, data types, groups of elements).
  • Include constraints to restrict valid data and ensure integrity.

Operations on Data Models

• Specify retrieval and updates.
• Types of operations include:
  • Basic operations: insert, delete, update.
  • User-defined: e.g., computestudentgpa, update_inventory.

Categories of Data Models

1. Conceptual Models: High-level view representing how users perceive data (e.g., ER model).
2. Physical Models: Low-level detail on how data is stored (e.g., files, indexes).
3. Implementation Models: Bridges between conceptual and physical, used in commercial DBMS (e.g., relational model).
4. Self-Describing Models: Embed data description with data values (e.g., XML).

Schemas vs. Instances

• Schema: Definition of database (structure, data types, constraints).
• Database State: Actual data at a point in time; dynamic and changes frequently.
• Valid State: State that adheres to schema constraints.

Three-Schema Architecture

• Purpose: Achieve program-data independence and multiple data views.
• Three Levels:
  • Internal Schema: Physical storage structure.
  • Conceptual Schema: Overall structure for user community.
  • External Schemas: User-specific views.

Data Independence

• Logical Data Independence: Change conceptual schema without altering external schemas.
• Physical Data Independence: Update internal schema without modifying conceptual schema.

DBMS Languages

1. Data Definition Language (DDL): Defines schema; used by DBA.
2. Data Manipulation Language (DML): For data retrieval and updates, can be embedded in applications.
3. High-Level Languages: Non-procedural languages like SQL.
4. Low-Level Languages: Procedural languages for detailed operations.

DBMS Interfaces

• Stand-alone and mobile interfaces, user-friendly with forms, graphics, and natural language queries.
• Programmer Interfaces: Allow embedding of DML in client language, e.g., JDBC, ODBC.

Database Utilities and Tools

• Include data loading, backup, performance monitoring, reporting, and data dictionary management.

Centralized vs. Client-Server Architectures

• Centralized DBMS: Single system for all components; remote access possible.
• Client-Server Architecture: Specialized servers providing services to clients, enhancing modularity and efficiency.
  • Two-tier and Three-tier architectures allow for enhanced data security and structured processing.

Classification of DBMSs

• Based on data models: e.g., relational, object-oriented, NoSQL.
• Others: single-user vs. multi-user, centralized vs. distributed.

Historical Context of Data Models

• Evolution from network and hierarchical to relational and object-oriented models.
• Current trends incorporate various data types and structures within relational systems.

Conclusion

• Understanding different data models, architectures, and languages is vital for managing databases effectively.