Month: January 2025

The Multidimensional data cube is a multi-dimensional array of data used for OLAP (Online Analytical Processing)

A Multidimensional data cube allows data to be viewed in multiple dimensions.

1. Roll-Up or Drill Up Operation

Roll-up is an aggregation operation that summarizes data by climbing up a concept hierarchy or by dimension reduction. It’s like zooming out to see a broader view.

Example: Sales data cube with dimensions: Location (City), Time (Month), and Product.

A roll-up operation might aggregate sales data from the city level to the country level.

Below Data Before Roll-Up:

• City: Sales in New York, Los Angeles, Chicago
• Month: January, February, March
• Product: Laptops, Tablets, Phones

Below Data After Roll-Up:

• Country: Sales in USA
• Quarter: Q1
• Product: Laptops, Tablets, Phones

2. Drill-Down Operation

Drill-down is the reverse of roll-up. It provides more detailed data by descending a concept hierarchy or adding dimensions. It’s like zooming in to see finer details.

Example: Using the same sales data cube, a drill-down operation might break down sales data from the country level to the city level.

Below Data Before Drill-Down:

• Country: Sales in USA
• Quarter: Q1
• Product: Laptops, Tablets, Phones

Below Data After Drill-Down:

• City: Sales in New York, Los Angeles, Chicago
• Month: January, February, March
• Product: Laptops, Tablets, Phones

3. Slice Operation

Slice selects a single dimension from the data cube, creating a sub-cube by fixing a value for one dimension.

Selection on one dimension of the given cube, resulting in a sub cube.

Example: If we want to analyze sales data for January only, we perform a slice operation on the Time dimension.

Below Data Before Slice:

• City: Sales in New York, Los Angeles, Chicago
• Month: January, February, March
• Product: Laptops, Tablets, Phones

Below Data After Slice:

• City: Sales in New York, Los Angeles, Chicago
• Month: January
• Product: Laptops, Tablets, Phones

4. Dice Operation

Dice selects two or more dimensions to create a sub-cube by fixing values for those dimensions.

Selection on two or more dimension of the given cube, resulting in a sub cube.

Example: If we want to analyze sales data for January and February in New York and Los Angeles, we perform a dice operation.

Below Data Before Dice:

• City: Sales in New York, Los Angeles, Chicago
• Month: January, February, March
• Product: Laptops, Tablets, Phones

Below Data After Dice:

• City: Sales in New York, Los Angeles
• Month: January, February
• Product: Laptops, Tablets, Phones

A Multidimensional Data Model:: It is defined as a Data Model that allows data to be organized and viewed in multiple dimensions, such as time, item, branch, and location, enabling organizations to analyze relationships between different perspectives and entities efficiently.

A multidimensional data model views data in the form of a data cube, which allows data to be modeled and viewed in multiple dimensions. The key components are:

• Dimensions: These are the perspectives or entities concerning which an organization keeps records. For example, time, item, and location.
• Facts / Measures: These are the numerical measures or quantities. For example, sales amount.

Data Cube:: It is a multi-dimensional data structure. A data cube is organized by its dimensions (as Products, States, Date)

A data cube allows data to be viewed in multiple dimensions.

Example

A Retail store that wants to analyze its sales data. The dimensions could be:

• Time: Year, Quarter, Month
• Item: Product Category, Product Name
• Location: City, Store

Dimensions:

• Time: Q1, Q2, Q3, Q4
• Item: Electronics, Clothing, Groceries
• Location: New York, Los Angeles, Chicago