{"title":"UCOS:通过用户聚类增强在线天际线计算","authors":"Kehan Chen, Lichuan Ji, Kunyang Jia, Jian Wu","doi":"10.1109/SCC.2013.14","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a skyline computation system UCOS (User Clustering based Online Skyline), which divides the computation into offline and online stages. Based on the truth that QoS similarity implies the skyline similarity, the offline stage of UCOS system dose user clustering according to the historical user-service QoS records by given distance metrics. Then, we compute the representative skyline for each cluster standing for the general characters of the users' skylines. Benefit from those offline results, the online stage is able to give a rapid prediction for online skyline request and achieves good online computation performance by doing refinement on the predicted results.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"UCOS: Enhanced Online Skyline Computation by User Clustering\",\"authors\":\"Kehan Chen, Lichuan Ji, Kunyang Jia, Jian Wu\",\"doi\":\"10.1109/SCC.2013.14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a skyline computation system UCOS (User Clustering based Online Skyline), which divides the computation into offline and online stages. Based on the truth that QoS similarity implies the skyline similarity, the offline stage of UCOS system dose user clustering according to the historical user-service QoS records by given distance metrics. Then, we compute the representative skyline for each cluster standing for the general characters of the users' skylines. Benefit from those offline results, the online stage is able to give a rapid prediction for online skyline request and achieves good online computation performance by doing refinement on the predicted results.\",\"PeriodicalId\":370898,\"journal\":{\"name\":\"2013 IEEE International Conference on Services Computing\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Services Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCC.2013.14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Services Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCC.2013.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本文提出了一种基于用户聚类的在线天际线计算系统UCOS (User Clustering based Online skyline),该系统将计算分为离线和在线两个阶段。基于QoS相似度意味着天际线相似度的事实,UCOS系统的离线阶段根据给定距离度量的历史用户服务QoS记录进行用户聚类。然后,我们计算代表用户天际线一般特征的每个集群的代表性天际线。利用这些离线结果,在线阶段能够对在线天际线请求进行快速预测,并通过对预测结果进行细化,获得良好的在线计算性能。
UCOS: Enhanced Online Skyline Computation by User Clustering
In this paper, we propose a skyline computation system UCOS (User Clustering based Online Skyline), which divides the computation into offline and online stages. Based on the truth that QoS similarity implies the skyline similarity, the offline stage of UCOS system dose user clustering according to the historical user-service QoS records by given distance metrics. Then, we compute the representative skyline for each cluster standing for the general characters of the users' skylines. Benefit from those offline results, the online stage is able to give a rapid prediction for online skyline request and achieves good online computation performance by doing refinement on the predicted results.