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

The Conceptual Integration Modeling Framework: Abstracting from the Multidimensional Model