An InfoCube can store a value for any combination of its dimension’s
members– for example a three dimensional InfoCube has a cell for any *(month,
product, customer)* triplet. The virtual size of an InfoCube is the
maximum number of cells it has. For example considering 24 months, 500
products and 1000 customers, the virtual size of a month-product-customer
InfoCube is of 24x500x1000=12,000,000 cells. After loading data into the
InfoCube, generally only a small fraction of the total InfoCube cells
really contain data. The ratio between the actual number of cells containing
data and the total number of cells of the InfoCube (obtained multiplying
the number of members of each dimension) is the InfoCube density.

Board does not create a cell for any possible value of an InfoCube’s dimensions. Different compression methods exist. The most significant method is the sparse management.

Let’s consider a company which has a large number of both products and
customers, a food and beverages company for example, selling to small
retail shops, restaurants, hotels, catering companies, hospitals, schools
etc. If you consider an average customer, you will find that he is only
buying a small basket of products out of the entire products list. A hospital
will probably buy a different set of products to a hotel or a school.
A customer does not buy all possible products and vice-versa. When this
is true then we say that Customer
and Product are **sparse**.
If customer C1 buys product P1 then (C1-P1) is called a sparse combination.

A sparse structure is a combination of two or more entities (not hierarchically related) for which the number of distinct combination of values is small compared to the total number of potential combinations. Sparse combinations are created when data is loaded into InfoCubes. When a sparse structure is defined, disk space is allocated only for the sparse combinations created while loading, therefore disk space overhead is minimal.

Sparse structures are defined when creating InfoCube versions.

Time entities cannot be included in a sparse structure. Note that some degree of disk space compression also occurs on dimensions that are not part of a sparse structure.

When you create an InfoCube version with two or more dimensions, use the following guidelines to define a sparse structure:

- Ignore the time dimension.
- Define the InfoCube version dimensions, without setting any sparse entities.
- Identify the two biggest entities in terms of actual number of members. Ask the question ”will every possible combination of entity1-entity2 exist?”. If the answer is no then define the sparse structure ”entity1-entity2”. Generally you should define a sparse structure whenever two entities have more than 1000 members, or when one entity has several thousand members and the other a few hundreds. Note that it is better to define a sparse structure when it is not needed than the opposite therefore when you are in doubt, set the entities as sparse.

- Identify the next biggest entity and go through the same reasoning considering the sparse structure as a unique entity. If the combination of ”entity3” and ”entity1-entity2” is sparse then add entity3 to the sparse structure. Repeat this process for the other dimensions.
- There is a dimensional limitation in defining sparse structures,
therefore after adding an entity to a sparse structure, click
the OK button to verify you have not reached the limit. If
you have exceeded the limit, a warning message appears on
the InfoCube definition row. Refer to the
*InfoCubes and spares dimensional characteristics*paragraph for details on the sparse dimensional limit.