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16 Ch. 1 Introduction
EXHIBIT 1.7 SERVICE-ORIENTED DISCOVERY AND ANALYSIS MODELING
STATES
TO-BE MODELING STATE. Nearly all of today’s modeling languages offer design capabilities that
reflect a futuristic state of software architecture. These languages illustrate future scenarios; some
even introduce a road map for implementation. This modeling approach is focused on how a service
and its hosting environment “ought to be.” Thus past or current design considerations are disre-
garded. In other words, since the to-be modeling paradigm does not “remember” past or current
service life cycle states, the only documented aspect of the service design is the future or end state
of architecture formation.
The service-oriented discovery and analysis modeling venture obviously capitalizes on the
to-be view. Future depiction of a solution and implementation is vital to the analysis proposition
since it recommends nonexistent avenues for tackling organizational concerns.
USED-TO-BE MODELING STATE. Unlike the to-be modeling state, which is focused just on future
service architecture, the used-to-be method looks at the past. Therefore, the analysis proposition
diagram depicts the past state of service design, identifies best practices that led to architectural
decisions, describes technological preferences that were popular earlier in the service life span, or
elucidate business strategies that influenced service design. These aspects of analysis offer a great
deal of information about a service’s evolution and its hosting environment, business incentives,
return on investment, architecture strategies, and more.
AS-IS MODELING STATE. The as-is modeling state that is depicted in an analysis proposition di-
agram illustrates the current state of a service architecture. This is an intermediate condition of the
design that is clearly different from past and future planning. Thus the diagram represents a service’s
transformation between a selected point in the past and current implementation. This feature enables
analysts, architects, managers, developers, and modelers to understand the fundamental changes that
a service and its operating environment have undergone.
Such valuable information can be leveraged for learning from mistakes and errors that
have occurred due to vast reasons, such as misconstrued business requirements, miscalculated