Multiple Dimension Processing is also referred to as static data analysis because the data values do not change.
The role of information in today’s business environment has become so tremendous that business organizations can literally not function without it. In fact, companies are spending millions of dollars just to implement an enterprise data management system and thousands of dollars more for upgrades and maintenance.
Because of the rapid advancement of information technology, geographical boundaries have been broken down and companies can already have multiple presences in several states or countries while giving the impression that the company is operating under a large monolithic structure. This set up creates data management efficiency because the company is seamlessly bound together despite great distances while each geographical branch can each have certain degree of control over respective data resources.
But with such business environment, very high volumes of data are being handled every single minute by the whole enterprise information system. Different data sources may be in different platform resulting in disparate data. But this particular aspect of data disparity can be handled by the extract, load and transformed (ETL) process so that data are being processed in a unified format. But there are still many dimensions in an enterprise information system.
An enterprise information system which includes data mart, data stores, business intelligence system and many other data handling and data processing parts exist primarily so that the business organization can be kept abreast with the latest information about the operations of the company and how it fares in the competition within the industry. As such, it needs to processes may data and their complex relationships and dimensions.
Generally, the databases which are configured for multiple dimension processing employ or are based on a multidimensional data model so that they can be manipulated with complex analytical and ad-hoc queries with ultra fast execution time.
The Online Analytical Processing (OLAP) processes multiple dimensions of data as this is part of a broader aspect of business intelligences while being able to function also with extract, load, transform (ETL), relational reporting and data mining. OLAP can derive relevant, meaningful and useful data from a wide variety of multiple data sources, databases, arrays and many other formats which are difficult to model.
OLAP take users a step beyond reporting and query tools. With the use of OLAP, the data is being represented in a multidimensional model rather than the traditional tabular data model. In comparison, the traditional data model defines the database schema focusing on the modeling process of a function and the viewing of information is with a set of transactions which are individually occurring at some single point in time.
On the other hand, the multidimensional model usually defines a star schema with data being viewed not as a single event but rather as a cumulative effects of events over a given period of time like weeks, month, years and so on.
It is also not surprising that there could be multiple ways of storing data including storing the data in a dedicated multidimensional database because OLAP tools do not indicate how the data will be actually stored.
A more complex form of OLAP is the MOLAP which stands for Multidimensional Online Analytical Processing. MOLAP is another analytic tool which has been specifically made for analyzing data dealing with multidimensional data model. MOLAP is also an alternative for another technology called ROLAP (Relational OLAP).
Both are almost the same in that they are analytic tools for multiple dimension processing, MOLAP significantly differs in that it requires storage information in the cube while having pre-computation. The data is stored by MOLAP in an optimized multi-dimensional array storage instead of storing in a relational database in the case of ROLAP.