This paper presents an approach for context modeling in complex self adapted
systems consisting of many independent context-aware applications. The
contextual information used for adaptation of all system applications is
described by an ontology treated as a global context model. A local context
model tailored to the specific needs of a particular application is defined
as a view over the global context in the form of a feature model. Feature
models and their configurations derived from the global context state are
then used by a specific dynamic software product line in order to adapt
applications at runtime. The main focus of the paper is on the realization of
mappings between global and local contexts. The paper describes an overall
model architecture and provides corresponding metamodels as well as rules for
a mapping between feature models and ontologies.
Heuristic Feature Models for Detection of Disrupted Markov Patterns
