Introduction to Multi Dimensional Databases

An overview of multi-dimensional databases and their usage.

1. Introduction to Multi-Dimensional Databases

Evolving from econometric research conducted in MIT in the 1960s, the multi-dimensional database has matured into the database engine of choice for data analysis applications. This application category is commonly referred to as OLAP (On-Line Analytical Processing). The multi-dimension database has become popular with industry because it allows high performance access and analysis of large amounts of related data across several applications, operating in different parts of the organization. Given that all business applications operate in a multi-tier environment, and often use different technologies operating on different platforms, it is important that such widely dispersed data can be accessed and analysed in a meaningful way.

The multi-dimensional database may also offer a better concept for visualising the way we already think of data in the real world. For example, most business managers already think of data in a multi-dimensional way, such as when they think of specific products in specific markets over certain periods of time. The multi-dimensional database attempts to present such data to the end user in a useful way.

1.1 Overview of a Multi-Dimensional Database System

Relational databases store data in a two dimensional format, where tables of data are presented as rows and columns. Multi-dimensional database systems offer an extension to this system to provide a multi-dimensional view of the data (Rand). For example, in multi-dimensional analysis, data entities such as products, regions, customers, dates etc. may all represent different dimensions. This intrinsic feature of the database structure will be covered in depth in subsequent sections of this paper.

Some further advantages to this database model are:

  • The ability to analyse large amounts of data with very fast response times.
  • To "slice and dice" through data, and "drill down or roll up" through various dimensions of the defined data structure.
  • To quickly identify trends or problem areas that would have been otherwise overlooked in an industry environment.

Multi-dimensional data structures can be implemented with multi-dimensional databases, or else they can also be implemented in a relational database management system using such techniques as the "Star Schema" and the "Snowflake Schema" (Weldon 1995).

The Star Schema is a means of aggregating data based on a set of known database dimensions, attempting to store a multi-dimensional data structure in a two-dimensional relational database management system (RDBMS). The Snow Flake Schema is an extension of the Star Schema by the principal of applying additional dimensions to the Star Schema in a RDBMS.

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John Collins

I have been writing about web technology and software development since 2001. I am the developer of the Alpha Framework for PHP. I love open source, technology, and economics. You can follow updates from this blog on Twitter.