In the data warehouse theory a common term to describe the logical representation of data is the OLAP cube. OLAP cubes can be thought of as extensions to the two-dimensional array of a spreadsheet as the data will be arranged as an element of a multi-dimensional cube1). Often OLAP is described as a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user2). Practical Example in a company: An analysis of financial data by product, by type of revenue, by cost, by city, and by comparing actual data with a budget.
A cube contains measures, dimensions, hierarchies and levels3). The numeric facts you will find in a cube are called measures and are categorized by dimensions. The cube structure is often created from a star schema or snowflake schema of tables in a relational database. Each of the elements of a dimension could be summarized using a hierarchy. An example for hierarchies could be: May 2009 is summarized into Second Quarter 2009, which again is summarized in the Year 2009.
Each individual point in a cube is referred to as a cell. Due to that fact, cubes support the following basic operations4):
Every dimension represents one 'variable', which can be analyzed separately. Due to this possibility a cube can be used to answer countless questions. For example:
How many books did we sell last week in Berlin?
How many books are in stock?
Which shop sold most books?
Which shops increased its sales within the past 3 years?
The data will be saved in a multi-dimensional way (MOLAP), a relational approach (ROLAP) or in hybrid-configuration (HOLAP)5). Traditional OLAP products are also called “multidimensional OLAP” (MOLAP) because they summarize transactions into multidimensional views ahead of time. 6) The data is organized into a cube structure. The cube can be rotated by the user, which is of help for example for financial summaries. Relational OLAP tools extract data from relational databases.7) ROLAPs are usually using data that has a large number of attributes, which is the reason why the data cannot be easily placed into a cube structure. For example, customer data with numerous descriptive fields are typically ROLAP candidates, rather than financial data. HOLAP (Hybrid Online Analytical Processing) is a combination of ROLAP and MOLAP. HOLAPs store one part of the data in a MOLAP and another part in a ROLAP system. This allows a tradeoff of the advantages of each.