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Most of current service selection approaches in service-oriented environments fail to capture the dynamic relationships between services or assume the complete knowledge of service composition is known as a prior. In these cases, problems may arise when consumers are not aware of the underlying composition behind services. The authors propose a distributed trust-aware service selection model based on a Bayesian network for consumers to maintain their knowledge of the environment locally. Results show the model can punish and reward services in terms of QoS properties accurately with incomplete observations so that consumers can prevent themselves from interacting services with unsatisfying QoS.
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