Introduction to advance data models such as object relational, object oriented

🌐 Introduction to Advanced Data Models

In traditional Relational Database Management Systems (RDBMS), data is stored in tables (relations), and the model is based on first normal form (1NF) — i.e., atomic data values. While relational models are powerful, they have limitations in handling complex data types like multimedia, engineering designs, geographic data, etc.

To overcome these limitations, advanced data models were developed, such as:

• Object-Oriented Data Model (OODM)
• Object-Relational Data Model (ORDM)

These models combine the strengths of object-oriented programming and relational databases to support more complex applications.

📦 1. Object-Oriented Data Model (OODM)

✅ Definition:

An Object-Oriented Data Model integrates object-oriented programming concepts with database technology. It treats data as objects (similar to object-oriented programming languages like Java or C++), supporting features like inheritance, encapsulation, and polymorphism.

🔍 Key Concepts:

1. Objects: An object is an instance of a class containing data (attributes) and behavior (methods).
2. Classes: A class defines a blueprint for objects — it contains attributes and methods.
3. Encapsulation: Combines data and the code that manipulates it into a single unit.
4. Inheritance: Allows classes to inherit attributes and behaviors from other classes.
5. Polymorphism: Allows objects to be treated as instances of their parent class rather than their actual class.

📊 Structure of an OODBMS:

• Stores objects directly, including complex data types.
• Objects can be persistent (they exist beyond the execution of the program).
• Relationships between objects can be represented directly (no need for foreign keys).

🛠️ Features:

• Suitable for applications with complex data, e.g., CAD/CAM, AI, multimedia.
• Improved semantic representation of data.
• Can store methods/functions inside the database.

🔗 Examples of OODBMS:

• ObjectDB
• db4o
• Versant
• GemStone

🔄 2. Object-Relational Data Model (ORDM)

✅ Definition:

An Object-Relational Data Model is a hybrid model that extends the relational model by integrating object-oriented features. It combines the simplicity and standardization of relational databases with the power and flexibility of object-oriented concepts.

🔍 Key Features:

1. User-Defined Types (UDTs): Allows users to define complex data types.
2. Inheritance: Supports subtypes and supertypes in tables.
3. Table Inheritance: Tables can inherit properties from other tables.
4. Nested Tables & Arrays: Enables storing complex and multivalued attributes.
5. Object Identity: Each object has a unique identifier (OID).
6. Encapsulation: Data and methods can be combined.

🏗️ Structure:

• Based on relational tables but enhanced to support object types.
• Rows (tuples) can contain complex objects, not just atomic values.
• Methods (functions) can be associated with data types.

⚙️ Implemented By:

Most modern RDBMSs support object-relational features, such as:

• Oracle (from version 8i and above)
• PostgreSQL
• IBM DB2
• Informix

🆚 Comparison with Relational Model:

⚖️ Object-Oriented vs. Object-Relational Models

✅ Summary

📘 Example: Object-Relational Extension in SQL (Oracle/PostgreSQL)

-- Create a user-defined object type
CREATE TYPE Address AS OBJECT (
    street VARCHAR2(50),
    city VARCHAR2(30),
    zip INT
);

-- Create a table using the object
CREATE TABLE Customers (
    id INT PRIMARY KEY,
    name VARCHAR2(50),
    address Address
);

This allows the use of complex types inside relational tables.

📝 Conclusion

In Advanced DBMS, understanding these advanced data models is essential because they allow databases to handle more complex and real-world applications effectively. While object-oriented models are more suited to applications requiring tight integration with OOP, object-relational models provide a practical balance between traditional relational systems and modern application needs.