Lastly, let us consider synthetic-a-posteriori-based creative knowledge.
This category includes pieces of knowledge that emerge owing to
pragmatic individual preferences, which are led by data- or
experience-driven activities. For example, consider a signal (time
series) that provides information pertaining to surface heights of an
arbitrary machined surface, as depicted in Figure 8(a ). Some
pieces of knowledge underlying the signal are given by the concept map,
depicted in Figure 8(b ), which boils down to following
statements—(1) A signal (time series) may comprise three stochastic
features—trends, noises, and bursts; (2) The stochastic features can
be defined using functions T , N , and B ,
respectively; (3) Functions T , N , and B can be
added to yield function S given by S = T + N+ B ; and (4) S can be used to simulate signals (time
series). The first statement is an example of
synthetic-a-posteriori-based creative knowledge, because it seems (to an
individual) that the given signal (Figure 8(a )) is caused by the
stochastic features (trends, noises, and bursts)Other individuals might
imagine it differently. The second and third statements represent pieces
of definitional knowledge, whereas the last statement qualifies as
analytic-a-priori-based creative knowledge, since it is yet unclear if
signals can be accurately simulated by the function S .