Effective Service Discovery based on Pertinence Probabilities Learning

Abstract

Web service discovery is one of the most motivating issues of service-oriented computing field. Several approaches have been proposed to tackle this problem. In general, they leverage similarity measures or logic-based reasoning to perform this task, but they still present some limitations in terms of effectiveness. In this paper, we propose a probabilistic-based approach to merge a set of matching algorithms and boost the global performance. The key idea consists of learning a set of relevance probabilities; thereafter, we use them to produce a combined ranking. The conducted experiments on the real world dataset "OWL-S TC 2" demonstrate the effectiveness of our model in terms of mean averaged precision (MAP); more specifically, our solution, termed "probabilistic fusion", outperforms all the state of the art matchmakers as well as the most prominent similarity measures.

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

“…With the advent of cloud computing and specifically online services (SaaS), it becomes more challenging to discover and select the best services with respect to the user's requirements [1,2]. Broadly speaking, we observe that a given functionality can be fulfilled by numerous SaaS with a variety of QoS levels.…”