一种基于全域子空间分解挖掘的QoS准确预测方法

Accurate Prediction Method of QoS Based on Global Subspace Decomposition Mining

QoS的准确预测是评判和选择最佳Web服务的一种重要标准;传统的QoS预测方法采用时间平均值和各种参数简单加权的方法,无法对大量Web服务下的资源进行准确预测,预测结果模糊;提出一种基于全域子空间分解挖掘的QoS准确预测方法,即采用全域分析的思想对所有数据进行预处理,在此基础上,通过子空间分解的方法,在子空间中对数据进行分解分析,提取数据的深层次特征,然后将全域分析的结果与子空间分解分析的结果进行有效的数据融合,从而实现对所分析数据的准确预测;采用一组Web节点和拟定度量参数进行了预测实验,结果显示,基于全域子空间分解挖掘的QoS预测方法可以精确预测出渐变过程,结果准确,在QoS预测中具有广泛的应用价值。

The accurate prediction of QoS is an important criterion for the Web services. In traditional QoS prediction method, when the time-weighted average and simple weight of various parameters are used, but a large number of Web services can not be accurately predicted, and the outcome is vague. An accurate prediction method of QoS based on glob- al subspace decomposition mining was proposed,and the idea of global analysis of all data preprocessing was done, on this basis, through subspace methods, the subspace decomposition analysis of the data was carried out and the depth fea- tures of the data were extracted, then the global result of the analysis and subspace decomposition analysis results were fuzzed for effective data fusion and achieving the analytical data valid mining and predictive. A set of Web nodes and metrics were used to do the prediction experiment, and the result shows that with the global subspace decomposition mining method, the gradual process can be predicted with detailed process. The result is accurate and it has wide appli- cation value in QoS prediction.