Primary events are the elemental information which can be represented (in the cube
metaphor, they correspond to the cube cells). In the invoice example they model the
invoicing of one product to one customer made by one agent on one day; it is not
possible to distinguish between invoices possibly made with different types (e.g.,
active, passive, returned, etc.) or in different hours of the day.
Guideline.3:.If the granularity of primary events as determined by the set
of dimensions is coarser than the granularity of tuples in the data source,
measures should be defined as either aggregations of numerical attributes
in the data source, or as counts of tuples.
Remarkably, some multidimensional models in the literature focus on treating
dimensions and measures symmetrically (Agrawal et al., 1995; Gyssens & Lakshmanan,
1997). This is an important achievement from both the point of view of
the uniformity of the logical model and that of the flexibility of OLAP operators.
Nevertheless we claim that, at a conceptual level, distinguishing between measures
and dimensions is important since it allows logical design to be more specifically
aimed at the efficiency required by data warehousing applications.
Aggregation is the basic OLAP operation, since it allows significant information
useful for decision support to be summarized from large amounts of data.
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