Handling inter-dimensional members dependency and reducing cube sparsity using reference dimensions in Analysis Services 2005
Traditionally the cubes structure is designed based on either star or snowflake schema. The dimensions in the cube is totally independent of each other and in the results can be obtained for any dimension members to any dimension. This is a classic illusion of an ideal world, i.e. even though the intention is good but the performance suffers since the cube is very sparse.
High cube sparsity adversely affect the MDX query performance. A very sparse cube can take longer to resolve calculated members and calculated cells, and MDX functions involving empty cells, such as CoalesceEmpty or NonEmptyCrossjoin, take slightly longer to process because of the large volume of empty cells that must be considered by such functions.
Tags: design