Capturing and Using QoS Relationships to Improve Service Selection
CAiSE '08 Proceedings of the 20th international conference on Advanced Information Systems Engineering
Preference-Aware Web Service Composition Using Hierarchical Reinforcement Learning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Selecting and ranking business processes with preferences: an approach based on fuzzy sets
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
A cooperative answering approach to fuzzy preferences queries in service discovery
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Adding non-functional preferences to service discovery
ICWE'12 Proceedings of the 12th international conference on Web Engineering
User-centered design of a QoS-based web service selection system
Service Oriented Computing and Applications
Systematic Approach for QoS Estimation of Web Services
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Qualitative preference-based service selection for multiple agents
Web Intelligence and Agent Systems
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The selection of web services according to different quality of service (QoS) is one of the most important decision issues for which complex considerations are involved. In many cases, the values for the qualitative QoS criteria are often imprecisely defined or acquired. Moreover, it is also not easy to accurately quantify the weight of each QoS criterion since human judgments including preference are often vague. In this paper, we apply the fundamental principles in the fuzzy set theory and model the decision making problem as Fuzzy Multiple Criteria Decision Making (FMCDM). The main contribution of this paper is to balance the subjective weight which reflects human rating and objective weight which represents reliability of evaluation criteria to form a synthetic weight. The detailed analysis of the synthetic weight for QoS-aware web service selection application is also presented.