Prediction of Failure Risk Through Logical Decision Trees in Web Service Compositions
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.
KeywordsService Composition Service Failure Failure Risk Service Execution Composition Process
This paper is supported by the project “programa de fortalecimiento del grupo de investigación Sistemas Inteligentes Web—SINTELWEB” quipu code 20201009532.
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