A time series and reduction based model for QoS prediction of service ontologies

In this study, we introduce a dynamic framework to predict the runtime QoS of OWL-S ontologoies by employing an Autoregressive-Moving-Average Model and QoS reduction rules. In the case study of a real-world ontology sample, a comparison between existing approaches and the proposed one is presented and results suggest that the proposed one achieves higher prediction accuracy.