Prediction of failure risk through logical decision trees in web service compositions

Abstract

In a service composition, the Quality of Services can be useful to identify those hidden data for a traditional composition; they can be a decisive factor for determining the behavior of future compositions since they allow evaluating risks resulting from reasons totally dependent on both the service environment and/or the composition system. Importance of this data is reflected on the way they are obtained, estimated, and applied to a composition. This paper has specifically studied the following three characteristics: availability, reactivity of services in periods of time, and management of beliefs to determine influence of services composition and to determine failure risk in such a composition through machine learning. © 2013 Springer Science+Business Media.