Neural Networks
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Improving the service time of web clients using server redirection
ACM SIGMETRICS Performance Evaluation Review
An Overview of Standards and Related Technology in Web Services
Distributed and Parallel Databases
Computational Statistics & Data Analysis - Nonlinear methods and data mining
IEEE Internet Computing
Quality driven web services composition
WWW '03 Proceedings of the 12th international conference on World Wide Web
A model for web services discovery with QoS
ACM SIGecom Exchanges
Software reliability prediction by soft computing techniques
Journal of Systems and Software
Investigating web services on the world wide web
Proceedings of the 17th international conference on World Wide Web
Software development cost estimation using wavelet neural networks
Journal of Systems and Software
Verity: a QoS metric for selecting Web services and providers
WISEW'03 Proceedings of the Fourth international conference on Web information systems engineering workshops
Empower service directories with knowledge
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Enabling web service discovery in heterogeneous environments
International Journal of Metadata, Semantics and Ontologies
Future Generation Computer Systems
Hi-index | 12.06 |
The web services, a novel paradigm in software technology, have innovative mechanism for rendering services over diversified environment. They promise to allow businesses to adapt rapidly to changes in the business environment and the needs of different customers. The rapid introduction of new web services into a dynamic business environment can adversely affect the service quality and user satisfaction. Consequently, assessment of the quality of web services is of paramount importance in selecting a web service for an application. In this paper, we employed well-known classification models viz., back propagation neural network (BPNN), probabilistic neural network (PNN), group method of data handling (GMDH), classification and regression trees (CART), TreeNet, support vector machine (SVM) and ID3 decision tree (J48) to predict the quality of a web service based on a set of quality attributes. The experiments are carried out on the QWS dataset. We applied 10-fold cross-validation to test the efficacy of the models. The J48 and TreeNet techniques outperformed all other techniques by yielding an average accuracy of 99.72%. We also performed feature selection and found that web-services relevance function (WSRF) is the most significant attribute in determining the quality of a web service. Later, we performed feature selection without WSRF and found that Reliability, Throughput, Successability, Documentation and ResponseTime are the most important attributes in that order. Moreover, the set of 'if-then' rules yielded by J48 and CART can be used as an expert system for web-services classification.