  Missing values occur commonly in the multidimensional data warehouses. They
may generate problems of usefulness of data since the analysis performed on a
multidimensional data warehouse is through different dimensions with
hierarchies where we can roll up or drill down to the different parameters of
analysis. Therefore, it's essential to complete these missing values in order
to carry out a better analysis. There are existing data imputation methods
which are suitable for numeric data, so they can be applied for fact tables but
not for dimension tables. Some other data imputation methods need extra time
and effort costs. As consequence, we propose in this article an internal data
imputation method for multidimensional data warehouse based on the existing
data and considering the intra-dimension and inter-dimension relationships.
