  Data warehouses are overwhelmingly built through a bottom-up process, which
starts with the identification of sources, continues with the extraction and
transformation of data from these sources, and then loads the data into a set
of data marts according to desired multidimensional relational schemas. End
user business intelligence tools are added on top of the materialized
multidimensional schemas to drive decision making in an organization.
Unfortunately, this bottom-up approach is costly both in terms of the skilled
users needed and the sheer size of the warehouses. This paper proposes a
top-down framework in which data warehousing is driven by a conceptual model.
The framework offers both design time and run time environments. At design
time, a business user first uses the conceptual modeling language as a
multidimensional object model to specify what business information is needed;
then she maps the conceptual model to a pre-existing logical multidimensional
representation. At run time, a system will transform the user conceptual model
together with the mappings into views over the logical multidimensional
representation. We focus on how the user can conceptually abstract from an
existing data warehouse, and on how this conceptual model can be mapped to the
logical multidimensional representation. We also give an indication of what
query language is used over the conceptual model. Finally, we argue that our
framework is a step along the way to allowing automatic generation of the data
warehouse.
