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Figure 7. Overview of the frameworks architecture
ated process. The client request is then transferred
to the new end point; and the client will be pro-
vided with a personalized process that takes into
account her context and preferences. In Apto, we
use the model-to-code transformation that takes
as input the CAAP model and generates code in
an executable language (e.g. BPEL). For more
details the reader is referred to a previous paper
(Jaroucheh et al. July 2010).
Finally, run-time support for context-aware
adaptive processes, as well as for related tasks
such as management of context, is provided by a
software infrastructure. Figure 7 shows an over-
view of the architecture of this infrastructure we
have developed as proof-of-concept.
this section describes a case study of different
applications supporting a conference event. The
key feature of the approach is the ability to sup-
port variable ontology reasoning in a distributed
dynamic environment. This means that properties
about a particular person, place and activity can
be described by distributed heterogeneous context
sources, and the contexts of these individual enti-
ties can be dynamically inferred through classifi-
cation. In addition, different applications register
their interest in the context information they need
by specifying the relevant context features i.e.
by configuring the context feature model. Given
the relevant context information the proposed
framework will be able to generate an application
process in response to the change of the context
information.
In pervasive computing environments e.g.
conference campus, sensors are often used to
detect the presence of people in rooms and build-
ings. For example, Bluetooth sensors can detect
CASE STUDY
In order to demonstrate the concepts and mod-
eling capabilities of the proposed frameworks,
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