Automatic Knowledge-driven Approach for Optimal Service Selection based on ELECTRE III and Quality-Aware Services

Abstract

Optimal real-time collection of a variety of environmental parameters from several environmental data sources, still remains a challenge in the selection process. Additionally, because environmental Web services, now, have access to a greater variety of environmental data sources, the quality of the services may differ even when they are functionally equivalent. Due to this competition, different environmental data sources compete to provide these functionally equivalent services with different levels of quality: the quality of services (QoS), as well as, the quality of the data sources themselves and their data (QoDS).Therefore, we present an approach to satisfy the need of ranking and selecting the optimal services. Our contribution is an automated knowledge-driven approach that relies on the ELECTRE III MCDM (Multi-Criteria Decision Making) method and on quality-aware service selection, to optimally select services.