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Service composition is emerging as an effective solution to ensure the integration of multiple atomic web services in order to create value-added customized services. However, the exploding number of the deployed service candidates that is constantly increasing makes the process of choosing the best service candidates an important challenge. When there are multiple web services that offer the same functionalities, we need to select the best one according to its non-functional criteria (e.g. response time, price, reliability). Skyline is a technique that helps reducing the size of our search space and comes as a complementary approach to the optimization methods. In fact, Skyline consists in preselecting the best candidates in the search space according to their non-functional criteria. Those web services are considered optimal as they are not dominated by any other point in the search space. Therefore, we will eliminate all the irrelevant web services which will considerably reduce the complexity of the computation. Most of the current Skyline computation relies on a strict dominance relationship called Pareto-dominance. In this paper, we propose a new method to compute the Skyline points with a fuzzy approach which allows taking into consideration the users preferences. We will through this paper show how we could construct a consistent fuzzy model to overcome the shortcomings of web service composition computation. A detailed study of the approach will demonstrate the effectiveness and the efficiency of the proposed algorithm.

Novel Approach for Computing Skyline Services with Fuzzy Consistent Model for QoS- based Service Composition