In Modern World, an exponential growth of web services is been observed over the Internet, This offers a big challenge for the optimal selection of the best web service among a group of web services with similar functionalities. The selection process must be made in order to determine which relevant Web services would satisfy a user`s needs. In this paper, a methodology for web service selection combines prefiltering followed by skyline selection. The K-Means clustering technique act as a prefiltering step for grouping similar web services based on the similar quality of service and filter out unrelated web services. From the set of filtered web services, Skyline technique will obtain a set of non-dominant web services and select the skyline services as the best candidate services. This Approach will reduce the searching space and increase the performance of the service selection. More precisely, we propose a model for the selection of Web services based on nonfunctional criteria of QoS. In order to show the feasibility performance study of the proposed architecture, the result of the test shows improvement in the web service selection.

Web Service Selection, Quality of Service (QoS), K-Means Clustering, Skyline

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