Time-Based QoS Prediction and Rank Aggregation of Web Services

Everyday activities are equipped with smart intellectual possessions in the modern Internet domain for which a wide range of web services are deployed in business, health-care systems, and environmental solutions. Entire services are accessed through web applications or hand-held computing devices. The recommender system is more prevalent in commercial applications. This research predicts the preference of consumers and lists the recommended services in order of ranking for consumers to choose services in a short time span. This proposed approach aims to offer the exact prediction of missing QoS (quality of service) value of web services at a specified time slice. The uncertainty of QoS value has been predicted using the cloud model theory. The focus is to give the global ranking using the aggregated ranking of the consumer's ranking list, which has been obtained through the Kemeny optimal aggregation algorithm. In this work, multidimensional QoS data of web services have experimented and given an accurate prediction and ranking in the web environment.