摘要：In order to accurately predict the Web service Qos in a highly dynamic environment,we put forward a Qos dynamic prediction method based on Semi-Markov Processes(SMP) and Case-based Reasoning(CBR).This method firstly uses semi-Markov process to predict business state of web service in the future, and then applies the technology of CBR to predict Web service QoS, for example,when the service deals with a specific task. Experimental results show that this prediction method can improve the accuracy of Web service QoS greatly. The results provides a reliable basis for the objective evaluation and successful Web service composition. entatio� grxS��#�segmentation algorithm based on iterative segmentation and maximum variance between clusters of traffic signs is studied. Secondly, with feature extraction of traffic signs based on SIFT studied, the codebook is generated by these feature clustering and images are described by histograms using Bag of Words (BOW) model. Finally, multi-class classifier based on SVM is designed to classify traffic signs. The experimental results demonstrate the effectiveness and practicality of the BOW model classification algorithm based on the traffic sign images collected in the natural environment.
关键词：Web Service;QoS(Quality of Service);Dynamic Prediction;Semi-Markov Processes;Case-based Reasoning
