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Data warehouses (DW) must integrate information from
the different areas and sources of an organization in order to extract knowledge relevant to decision-making.
The DW development is not an easy task, which is why various design approaches
have been put forward. These approaches can be classified in three different paradigms according to the origin of the information
requirements: supply-driven, demand-driven, and hybrids of these. This article compares the methodologies for
the multidimensional design of DW through a systematic mapping as
research methodology. The study is presented for each paradigm, the main characteristics of the
methodologies, their notations and problem areas exhibited in each one of them.
The results indicate that there is no
follow-up to the complete process of implementing a DW in either an academic or
industrial environment; however, there is also no evidence that the attempt is
made to address the design and development of a DW by applying and comparing
different methodologies existing in the field.