The rapid proliferation of new web services over the last decade has led to an increase in functionally identical services, making the service selection system more challenging. In this work, we propose a web service selection model consisting of two layers, which we call CPSky. The upper layer, called the Prefilter layer, filters and allows only potent services to participate in the selection process. Pruning and clustering based on the quality of service form the basis of prefiltering. The bottom layer, called the Selection layer, uses the proposed Skyline-Plus approach to select the appropriate web service. The existing skyline technique always generates the same set of skyline services and does not consider the end-user requested QoS. Additionally, the skyline results in a non-dominated set of services without any ordering of services. To address these issues, we propose a modified skyline, which we call Skyline-Plus. The Selection layer also identifies replaceable web services using the Pearson correlation coefficient. The proposed approach's efficacy is validated through experimental evaluation on a real-world dataset using four performance evaluation parameters. The experimental results show that the proposed approach performs better than existing similar approaches in terms of efficiency and end-user satisfaction.

Selection of an appropriate web service fulfilling the requirements of the end user is a challenging task. Most of the existing systems use Quality of Service (QoS) as predominant parameter for web service selection, without any preprocessing or filtering. These systems consider all of the candidate web services during selection process and require unnecessary processing of those web services which are far below the expectations of the end user. In this work, an approach for web service selection based on QoS parameters is proposed. The proposed method starts with prefiltering of candidate web services using classification technique. An improved PROMETHEE method, we call it as PROMETHEE Plus, is applied to most eligible web services and Maximizing Deviation Method based hybrid weight evaluation mechanism is adopted. Top-k web services matching closely with the QoS requirements of the end user are selected. Experiments on the dataset of real world web services are conducted. Experimental results show that our approach performs better in terms of end user satisfaction and efficiency with reference to the existing similar approaches.

Web services are important components of modern day software. Many services are composed on the fly to offer the required business functionality. Web services run in very dynamic environment and are prone to failure or run time change in service quality. On the occurrence of failure or unavailability of any component service, replacement by an equivalent service is required. In this paper, a web service selection approach by taking into cognizance the replaceability of service as the basis of selection is used. At the time of selection, the replaceability of a web service is determined using improved PROMETHEE method. The task of obtaining replacement service at run time is very expensive and challenging. The experimental results depict the improvements achieved in the results of selection. The experiments are conducted by taking QoS dataset of real world web services.

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.