Describe Multidimensional Data Cube

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 Multidimensional data cube

It basically helps in storing large amounts of data by making use of a multi-dimensional array. It increases its efficiency by keeping an index of each dimension. Thus, dimensional is able to retrieve data fast.

 Multidimensional arrays are used to store data that assures a multidimensional view of the data. A multidimensional data cube helps in storing a large amount of data. Multidimensional data cube implements indexing to represent each dimension of a data cube which improves the accessing, retrieving, and storing data from the data cube.

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Centered on a structure where the cube is patterned as a multidimensional array, most OLAP products are created. Compared to other methods, these multidimensional OLAP (MOLAP) products typically provide a better performance, primarily because they can be indexed directly into the data cube structure to capture data subsets. The cube gets sparser as the number of dimensions is larger. That ensures that no aggregated data would be stored in multiple cells that represent unique note combinations. This in turn raises the storage needs, which can at times exceed undesirable thresholds, rendering the MOLAP solution untenable for massive, multi-dimensional data sets. Compression strategies may help, but their use may damage MOLAP's natural indexing.

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Multidimensional Data Cube: 


Most OLAP products are developed based on a structure where the cube is patterned as a multidimensional array. These multidimensional OLAP (MOLAP) products usually offer 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.

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