Short note on data cube.

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  • A data cube refers is a three-dimensional (3D) (or higher) range of values that are generally used to explain the time sequence of an image's data. It is a data abstraction to evaluate aggregated data from a variety of viewpoints. It is also useful for imaging spectroscopy as a spectrally-resolved image is depicted as a 3-D volume.
  • A data cube can also be described as the multidimensional extension of two-dimensional tables. It can be viewed as a collection of identical 2-D tables stacked upon one another. Data cubes are used to represent data that is too complex to be described by a table of columns and rows. As such, data cubes can go far beyond 3-D to include many more dimensions.
  • A data cube is generally used to easily interpret data. It is especially useful when representing data together with dimensions as certain measures of business requirements. A cube's every dimension represents certain characteristics of the database, for example, daily, monthly or yearly sales. The data included inside a data cube makes it possible to analyze almost all the figures for virtually any or all customers, sales agents, products, and much more. Thus, a data cube can help to establish trends and analyze performance.



Data cubes are mainly categorized into two categories:

a) Multidimensional Data Cube: Most OLAP products are developed based on a structure where the cube is patterned as a multidimensional array. This multidimensional OLAP (MOLAP) product usually offers improved performance when compared to other approaches mainly because they can be indexed directly into the structure of the data cube to gather subsets of data. When the number of dimensions is greater, the cube becomes sparser. That means that several cells that represent particular attribute combinations will not contain any aggregated data. This in turn boosts the storage requirements, which may reach undesirable levels at times, making the MOLAP solution untenable for huge data sets with many dimensions. Compression techniques might help; however, their use can damage the natural indexing of MOLAP.

b)  Relational OLAP: Relational OLAP makes use of the relational database model. The ROLAP data cube is employed as a bunch of relational tables (approximately twice as many as the number of dimensions) compared to a multidimensional array. Each one of these tables, known as a cuboid, signifies a specific view.

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