Clustering Based Approach for Web Service Selection Using Skyline Computations

Web services are useful to automate a task. Along with automation of task, efficiency improvement is another important challenge for researchers of web service community. To improve the overall execution efficiency of web service based system, the input to selection process needs to be preprocessed. In this work, the clustering is applied to candidate web services to determine similar services on the basis of QoS information. A systematic analysis is done to evaluate the performance of three clustering techniques using Dunn index and average distance measure. The best performing clustering technique is applied on candidate web services. The most prominent set of web services is considered for skyline based selection. To perform various experiments, a QoS dataset based on real world web services is used. It is evident from the results of experimentation that the proposed approach is better than existing similar approaches for web service selection.

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