Context-Aware Service Selection with Uncertain Context Information

Yves Vanrompay, Manuele Kirsch-Pinheiro, Yolande Berbers


The current evolution of Service-Oriented Computing in ubiquitous systems
is leading to the development of context-aware services. These are services
whose description is enriched with context information related to the service execution
environment and adaptation capabilities. This information is often used for discovery
and adaptation purposes. However, in real-life systems context information
is naturally dynamic, uncertain and incomplete, which represents an important issue
when comparing service description and user requirements. Uncertainty of context
information may lead to an inexact match between provided and required service
capabilities, and consequently to the non-selection of services. In order to handle
uncertain and incomplete context information, we propose a mechanism inspired
by graph-comparison for matching contextual service descriptions using similarity
measures that allow inexact matching. Service description and requirements are
compared using two kinds of similarity measures: local measures, which compare
individually required and provided properties, and global measures, which take into
account the context description as a whole. We show how the proposed mechanism
is integrated in MUSIC, an existing adaptation middleware, and how it enables more
optimal adaptation decision making.

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