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摘要
How to organize and manage Web services, and help users to select the atomic and a set of services with correlations to meet their functional and non-functional requirements quickly is a key problem to be solved in the era of services computing. Firstly, it uses the three-stage dependency Bayesian network structure learning method to organize service clusters which realize different functions. Then it uses the maximum likelihood estimation and Bayesian estimation methods to do the parameter learning, and the conditional probability table (CPT) of all the nodes can be got. This method can help users select a set of services with better function in the organized services quickly and accurately. Finally, the effectiveness of the proposed method is validated through experiments and case study.
