??? Preliminary identification of facts, dimensions, and aggregations: The
analyst tries to identify the underlying facts and dimensions from the queries.
This is a manual or semiautomatic process (for example, this process can make
use of one of the many algorithms that use an entity-relationship diagram for
obtaining the star schema for the data warehouse), which includes the validation
against the aggregations dictionary DIC_AGGR (updating this dictionary,
if necessary).
??? Quality.survey.interviews:.after the queries are validated, a list of data elements
will be extracted from the query definitions collected in the former step.
These are the data elements that will be required for answering the queries.
Recall that for each data element there is an entry in the data dictionary. The
quality requirements for these data elements is then defined and registered in
three forms: QRY_QTY I, QRY_QTY II, and QRY_QTY III. The first one
contains, for each query, the following information: (1) Query ID and (2)
Data ID: one for each data element in the query. This is the identifier of the
element in the data dictionary. All data elements directly or indirectly related
to the query must be included. For example, if a query asks for the ???Average
monthly sales,??? although it does not directly include the dollar value of each
sale, this value is involved in the computation of the average, so we need to
specify its quality requirement; (3) description of the element; (4) aggregation:
indicates if the data expresses a dimension level; (5) range (valid range for
the data element); (6) timeliness; and (7) accuracy.
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