{"title":"基于用户聚类和回归算法的QoS预测新方法","authors":"Yuliang Shi, Kun Zhang, Bing Liu, Li-zhen Cui","doi":"10.1109/ICWS.2011.95","DOIUrl":null,"url":null,"abstract":"QoS has become an important measure for web service selection. In this paper, we present an approach which can provide the approximate QoS value for users, and support finding the optimal web service. Firstly, it clusters the users based on location and network condition, then according to the QoS historical statistics of users in the same cluster, uses the linear regression algorithm to predict the QoS value based on invocation time and workload.","PeriodicalId":118512,"journal":{"name":"2011 IEEE International Conference on Web Services","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"A New QoS Prediction Approach Based on User Clustering and Regression Algorithms\",\"authors\":\"Yuliang Shi, Kun Zhang, Bing Liu, Li-zhen Cui\",\"doi\":\"10.1109/ICWS.2011.95\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"QoS has become an important measure for web service selection. In this paper, we present an approach which can provide the approximate QoS value for users, and support finding the optimal web service. Firstly, it clusters the users based on location and network condition, then according to the QoS historical statistics of users in the same cluster, uses the linear regression algorithm to predict the QoS value based on invocation time and workload.\",\"PeriodicalId\":118512,\"journal\":{\"name\":\"2011 IEEE International Conference on Web Services\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Web Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICWS.2011.95\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Web Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWS.2011.95","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New QoS Prediction Approach Based on User Clustering and Regression Algorithms
QoS has become an important measure for web service selection. In this paper, we present an approach which can provide the approximate QoS value for users, and support finding the optimal web service. Firstly, it clusters the users based on location and network condition, then according to the QoS historical statistics of users in the same cluster, uses the linear regression algorithm to predict the QoS value based on invocation time and workload.
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