Object-Oriented is a programming paradigm, not a data model.

A data model provides a blueprint for how data is organized and related, enabling efficient storage, retrieval, and analysis.

An ER diagram consists of entities (objects), attributes (properties of entities), and relationships (associations between entities).

Mapping involves translating the abstract concepts of a conceptual data model into the specific structures and constraints of a physical database.

2NF requires that all non-key attributes be fully dependent on the primary key, eliminating partial dependencies.

Data warehousing involves extracting, transforming, and loading (ETL) data from various sources into a central repository for analysis and reporting.

An ADW is a type of data warehouse that supports real-time data updates and transactions, enabling immediate data analysis and decision-making.

Normalization involves decomposing data into smaller, related tables to eliminate redundancy and improve data integrity.

Indexing, hashing, and clustering are all techniques used to optimize data access and improve query performance in relational databases.

Schema implementation involves creating the actual database tables, columns, and constraints based on the physical data model.

An Object-Oriented data model organizes data as a collection of objects, where each object has its own properties and methods.

Decomposition involves breaking down a large table into smaller, related tables to improve data organization and reduce redundancy.

A Network data model represents data as a collection of nodes (entities) and edges (relationships), allowing for complex connections between data items.

Data integration involves combining data from multiple sources into a single, consistent view, enabling comprehensive analysis and decision-making.

A Relational data model organizes data into tables, where each table consists of rows (records) and columns (attributes).