Predicting Quality of Web Service using IKS hybrid model

Abstract

Internet and various services offered by it has become a daily routine. The Quality of Web Service (QWS) has become a significant factor in distinguishing the success of service providers. The main purpose of this paper is to analyze quality prediction using the IKS hybrid model with a new approach of data classification. We present the IKS hybrid model. The model combines selection of features, clustering and classification techniques. Three techniques are used (Information Gain (IG), K-means and Support Vector Machine (SVM)) over QWS dataset with collected 5,000 web services. Our experiments and test results show that the proposed hybrid approach has achieved promising results in predicting the quality of web services and it represents a good basis for further development and research. Predicting the Quality of Web Service (QWS) has long been a topic studied along with the development of the internet and the emergence of various web services offered. With the rapid introduction of different web service technologies, cloud computing and internet communication, researchers have focused more on the functional aspects of web services. Due to the large number of web providers who offer web services it was necessary to define standards that will define the quality of web services. The Quality of Service (QoS) is a term that is closely associated with the evaluation of service quality. The QoS refers to network abilities, web provider's ability in providing quality services through a network traffic using various technologies. For the measurement of QoS in our study we have used the dataset with standard features (1),(2).