An Improved Fuzzy Analytical Hierarchy Process for K-Representative Skyline Web Services Selection

Nowadays, the processes of selecting web services which give the same functionality with different quality of service (QoS) become an important issue. To deal with the large number of Web services candidates, K-representative Skyline is appeared as a Skyline variant to find the short list of the most relevant Web services that represent a summary about the full skyline result. However, it returns generally a conflicting result. The AHP (Analytical Hierarchic Processes) method and its variants as Fuzzy AHP are widely used in ranking incomparable alternatives. However, it requires a huge number of inputs for users to fulfill a multiple comparison matrix, which make it difficult to use in practice notably in Web services selection field. In this work, we propose an improved Fuzzy AHP called IFAHP which allows to: i) elicit the QoS importance level using linguistic terms based on natural language, asking fewer efforts to users, ii) group the QoS attributes according to their importance level, iii) reduce the number of inputs and generate automatically all pair-wise matrix with respect to each attribute, using a discretization algorithm. The experimental evaluation conducted on real world dataset illustrates the feasibility and the effectiveness of our approach.