Neurons are the
interconnecting processing elements that yield data transformations and formalized outputs, and
they are akin to the proposed attribution model nodes, which link attributes by their associations.
Exhibit 4.4 depicts this concept. This attribution model example is made up of three
hierarchy levels. The first represents the initial core attribute group that was extracted earlier
from the business requirements, the second level identifies attribute variations, and the third is
a converging level in which all attributes are combined. This network grid ensures a patterned
relationship between its descending levels, because each node moving downward consists of the
attribute variations of the previous level. For example, level 2 is composed of three nodes that
represent the following attribute combinations: A + B, A + C, and B + C. This node variation is
established based on the previous level (level 1). Thus, following this pattern, the bottom level
converging node is made up of all previous attributes.
A three-level attribution model, as depicted in Exhibit 4.4 would not provide a great deal
of flexibility, because the number of attribute permutations may limit upcoming service discovery
and decision-making process. Thus, more levels and more attributes might be added to accurately
categorize the organization??™s business solutions. Exhibit 4.5 illustrates this idea, according to
which the attribution model can be expanded to multiple levels and can offer a wider selection
scope for future conceptual services.
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