In a data-driven approach, requirement analysis is typically carried out informally,
based on simple requirement glossaries (Lechtenb?¶rger, 2001) rather than on formal
diagrams. Conceptual design is then heavily rooted on source schemata and can be
largely automated. In particular, the designer is actively supported in identifying
dimensions and measures, in building hierarchies, in detecting convergences and
shared hierarchies. For instance, the approach proposed by Golfarelli et al. (1998)
consists of five steps that, starting from the source schema expressed either by an
E/R schema or a relational schema, create the conceptual schema for the DW:
1. Choose facts of interest on the source schema
2. For each fact, build an attribute tree that captures the functional dependencies
expressed by the source schema
3. Edit the attribute trees by adding/deleting attributes and functional dependencies
4. Choose dimensions and measures
5. Create the fact schemata
While step 2 is completely automated, some advanced constructs of the DFM are
manually applied by the designer during step 5.
On-the-field experience shows that, when applicable, the data-driven approach is
preferable since it reduces the overall time necessary for design. In fact, not only
conceptual design can be partially automated, but even ETL design is made easier
since the mapping between the data sources and the DW is derived at no additional
cost during conceptual design.
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