Web服务在可信性指标上的不完整、动态多变性给用户的服务选择与利用带来重要挑战。本项目针对服务可信性时间序列常出现的非平稳特征，拟运用小波分解技术对原始序列进行分层处理，再集成各层的预测结果给出服务在后续时间的可信性预测值。在Web服务可信性空缺记录预测方面，运用位置或关系网络感知的方法筛选出相似用户或服务，再试图在传统的协同过滤方法之外建立一种基于群体智能搜索的预测新途径。针对所获取的Web服务评价信息量少、不确定性强等问题，拟采用灰色系统理论来实施数据处理，在此基础上给出Web服务可信性的综合评价。依据观测得到的可信性指标分布与依赖关系，运用马尔可夫链蒙特卡罗方法进行随机抽样模拟出服务的可信性指标值，再通过聚合函数计算或Petri网模拟来评估整个Web服务系统的可信性。总之，通过上述基于智能处理与优化方法的研究将有利于进一步丰富服务可信性预测与评估的手段，并为实际应用提供有益借鉴。

Web services have become the primary source for constructing software system over Internet. However, some features of Web service's trustworthiness, such as incomplete, dynamic and changeable, bring great challenge to service selection and invocation. In order to deal with the nonstationary time series of Web service's trustworthiness, this project attempts to adopt wavelet decomposition technique to convert the original series into several layers. Subsequently, the prediction about trustworthiness on each layer can be employed by BP neural network or logistic regression analysis. The final prediction value for the trustworthiness at the next time can be achieved through integrating the results in each layer. For predicting the empty records in user-service trustworthiness matrix, similar users or services are firstly filtered out according to location information or relationship network, and then the missed values can be recommended by means of a swarm intelligence search-based method, which is significantly different from the traditional collaborative filtering approach. Due to the lack and uncertain information for trustworthiness evaluation, grey system theory is suggested for data processing so as to provide a comprehensive evaluation for Web services. According to the probability distribution of trustworthiness indicators and their dependences, Markov Chain Monte Carlo (MCMC) method is used to randomly sample and simulate the trustworthiness vectors for each single Web service. Then, the trustworthiness of whole Web service system can be yield by means of aggregate functions or Petri-Net simulation. In a word, researches in this project will enrich the ways to predict and evaluate the trustworthiness of Web services, and also will provide some references for the practice on Web services selection and utilization.