DATA WAREHOUSING AND MINIG LECTURE NOTES-- Multidimensional Data Analysis:

Multidimensional Data Analysis:

Multidimensional analysis is a data analysis process that groups data into two or more categories: data dimensions and measurements. For example, a data set consisting of the number of wins for a single football team at each of several years is a single-dimensional (in this case, longitudinal) data set. A data set consisting of the number of wins for several football teams in a single year is also a single-dimensional (in this case, cross-sectional) data set. A data set consisting of the number of wins for several football teams over several years is a two-dimensional data set.

In many disciplines, two-dimensional data sets are also called panel data. While, strictly speaking, two- and higher- dimensional data sets are multi-dimensional, the term multidimensional tends to be applied only to data sets with three or more dimensions. For example, some forecast data sets provide forecasts for multiple target periods, conducted by multiple forecasters, and made at multiple horizons. The three dimensions provide more information than can be gleaned from two dimensional panel data sets.

A multidimensional database (MDB) is a type of database that is optimized for data warehouse and online analytical processing (OLAP) applications. Multidimensional databases are frequently created using input from existing relational databases. Whereas a relational database is typically accessed using a Structured Query Language (SQL) query, a multidimensional database allows a user to ask questions like "How many Aptivas have been sold in Nebraska so far this year?" and similar questions related to summarizing business operations and trends. An OLAP application that accesses data from a multidimensional database is known as a MOLAP (multidimensional OLAP) application.

A multidimensional database - or a multidimensional database management system (MDDBMS) - implies the ability to rapidly process the data in the database so that answers can be generated quickly. A number of vendors provide products that use multidimensional databases. Approaches to how data is stored and the user interface vary.

Conceptually, a multidimensional database uses the idea of a data cube to represent the dimensions of data available to a user. For example, sales could be viewed in the dimensions of product model, geography, time, or some additional dimension. In this case, sales are known as the measure attribute of the data cube and the other dimensions are seen as feature attributes. Additionally, a database creator can define hierarchies and levels within a dimension (for example, state and city levels within a regional hierarchy).