Informational models representing context information in a well-defined structure.
Published in Chapter:
Service Selection with Uncertain Context Information
Yves Vanrompay (Katholieke Universiteit Leuven, Belgium), Manuele Kirsch-Pinheiro (Université Paris 1 Panthéon-Sorbonne, France), and Yolande Berbers (Katholieke Universiteit Leuven, Belgium)
Copyright: © 2012
|Pages: 24
DOI: 10.4018/978-1-61350-432-1.ch009
Abstract
The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. Context-aware services are services of which the description is enriched with context information related to non-functional requirements, describing the service execution environment or its 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 the service description with 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 this chapter, we focus on how to handle uncertain and incomplete context information for service selection. We consider this issue by presenting a service ranking and selection algorithm, inspired by graph-based matching algorithms. This graph-based service selection algorithm compares contextual service descriptions using similarity measures that allow inexact matching. The service description and non-functional 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.