Satisfaction-based Web service discovery and selection scheme utilizing vague sets theory
Most existing QoS-aware Service Discovery and Selection (QSDS) schemes use linguistic terms to describe the QoS satisfaction degree when ranking competing alternatives. However, such schemes determine the degree of QoS satisfaction using fuzzy set membership functions, and cannot therefore take negative evidence (i.e., consumer dissatisfaction) into account. By contrast, vague sets provide the ability to represent both positive and negative evidence when modeling uncertain objects. Accordingly, the present study proposes a new satisfaction-based Web service ranking method for QSDS problems involving a group of online service consumers with imprecise and inconsistent expectations and degrees of satisfaction regarding multiple QoS criteria. Importantly, the proposed approach overcomes the uncertainty regarding the difference between the users expectations and their degree of satisfaction inherent in DempsterShafer (D-S) evidence theory. Overall, the results show that the proposed scheme provides an effective means of solving the problem of vague and ill-defined information in the QSDS decision-making process.
service discovery vague sets qos satisfaction degree evidence theory