Explain the operation of data cube with suitable examples
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Explain the operation of data cube with suitable examples?

 operation of data cube

By:pallaviaithaln

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Data warehouses contain huge volumes of data.
OLAP servers demand that decision support queries be answered in the order of seconds.
Therefore, it is crucial for data warehouse systems
to support highly efficient cube computation techniques, access methods, and query processing techniques.
In this section, we present an overview of methods for the efficient implementation of data warehouse systems.
Efficient Computation of Data Cubes At the core of multidimensional data analysis is the efficient computation of aggregations across many sets of dimensions. In SQL terms, these aggregations are referred to as group -by’s. Each group-by can be represented by a cuboid, where the set of group-by’s forms a lattice of cuboids
defining a data cube.

In this section, we explore issues relating to the efficient computation of data cubes.
The Compute Cube Operator and the Curse of Dimensionality One approach to cube computation extends SQL so as to include a compute cube operator.
The compute cube operator computes aggregates over all subsets of the dimensions specified in the operation.
This can require excessive storage space, especially for  large numbers of dimensions.
We start with an intuitive look at what is involved in the efficient computation of data cubes.
Example: A data cube is a lattice of cuboids.
Suppose that you would like to create a data cube
for All Electronics sales that contains the following:
city, item, year, and sales in dollars.
You would like to be able to analyze the data, with queries such as the following:
“Compute the sum of sales, grouping by city and item.”
“Compute the sum of sales, grouping by city.”
“Compute the sum of sales, grouping by item.”


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