Predicting Quality of Web Service using IKS hybrid model

Author

Trstenjak, Bruno ; Donko, Dzenana

Author_Institution

Dept. of Comput. Eng., Medimurje Univ. of Appl. Sci., Cakovec, Croatia

fYear

2014

fDate

27-29 Oct. 2014

Firstpage

1

Lastpage

6

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.

Keywords

Web services; feature selection; pattern classification; pattern clustering; support vector machines; IG technique; IKS hybrid model; QWS; QWS dataset; SVM technique; Web service providers; classification technique; clustering technique; data classification; feature selection; information gain technique; k-means technique; quality prediction analysis; quality-of-Web service prediction; support vector machine technique; Classification algorithms; Clustering algorithms; Machine learning algorithms; Prediction algorithms; Predictive models; Support vector machines; Web services; Hybrid model; Information Gain; K-means; Predicting; QWS; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Telecommunications (BIHTEL), 2014 X International Symposium on

Conference_Location

Sarajevo

Print_ISBN

978-1-4799-8038-3

Type

conf

DOI

10.1109/BIHTEL.2014.6987636

Filename

6987636