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from those participant services such that the overall behavior corre-
sponds to that of the composite service. If a delegator exists, the behavior
of the target composite service can be achieved by synthesizing the
participant services and delegating their individual behaviors [40-42].
However, current solutions have limitations when they are used in
real business scenarios. First, most of them assume a coherent repre-
sentation of requirement (target service) and participant services. Few
of them consider the gap between business and service domains. As we
already saw earlier in this chapter, these two domains usually have
different domain models with different concerns. This gap hampers the
effective use of the AI-planning-, optimization-, and automata-based
approaches.
Second, many of the approaches use ontology to describe the
behavior of services and related data. However, nowadays, in most
enterprises, semantic information of their Web services is not always
available to users. The planning-based approach needs much addi-
tional effort in adding semantic tags on top of the current Web service
standard stack.
On one hand, current solutions need a more ideal usage scenario
than those in reality. On the other hand, service portfolio contains
abundant data to be investigated and used. We believe that from these
data, considerable guidance can be derived to improve service compo-
sition. We also believe that our data-driven solution is a lightweight
approach that can be used in conjunction with other approaches such as
planning or optimization-based ones.
