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.
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Acknowledgments
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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Portilla-Rosero, B., Guzmán, J.A., Alor-Hernández, G. (2013). Prediction of Failure Risk Through Logical Decision Trees in Web Service Compositions. In: Elleithy, K., Sobh, T. (eds) Innovations and Advances in Computer, Information, Systems Sciences, and Engineering. Lecture Notes in Electrical Engineering, vol 152. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3535-8_51
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DOI: https://doi.org/10.1007/978-1-4614-3535-8_51
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