大数据环境下基于K中心点优化算法的Web服务组合

在当前Web服务海量增加、现有Web服务选择算法低效、用户匹配度差的基础上，针对K中心点算法存在的质点偏移、准确率低和容易发生畸变等问题，提出一种大数据环境下基于K中心点优化算法的Web服务组合方法。该方法是在大数据环境下，根据不同用户需求满意度及Web服务QoS参数，对基于优化初始聚类中心的K中心点算法的Web服务选择及最优Web服务组合进行研究。同时针对不同的选择方法对服务动态选择及组合的准确度、迭代更新次数、候选集选择时间及选择总时间进行实验分析，验证了本文研究方法的有效性和可靠性。

Web Service Composition Based on K-medoidsPoint Optimization Algorithm in Big Data Environment

Based on the current increase in the mass of Web services, the inefficiency of existing Web service selection algorithms, and poor user matching, this paper proposes a solution to the problems of particle shift, low accuracy, and easy distortion in the K-medoids point algorithm. Research on Web service composition based on K-medoids point optimization algorithm is given in big data environment. The method is based on the study of Web service selection and optimal Web service combination based on the K-medoids point algorithm that optimizes the initial clustering center based on the satisfaction of different user needs and the QoS parameters of Web services in a big data environment. At the same time, the accuracy of dynamic selection and combination of services, the number of iteration updates, the selection time of candidate sets and the total selection time are experimentally analyzed for different selection methods, which verifies the effectiveness and reliability of the method in this paper.