{"title":"分析数据集群对业务的支持","authors":"Y. Zhao, Chi-Hung Chi, Chen Ding","doi":"10.1109/ICSESS.2011.5982246","DOIUrl":null,"url":null,"abstract":"With the current direction of service and cloud, unique characteristics of online software services impose new algorithmic requirements and cause differential applicability/ suitability of different clustering approaches in service analytics. In this paper, we investigate the efficiency and effectiveness of current important data clustering techniques, partitioning and hierarchical, for service analytics. It is our goal that results from this paper will serve as requirement guidelines for developing and deploying future intelligence services.","PeriodicalId":108533,"journal":{"name":"2011 IEEE 2nd International Conference on Software Engineering and Service Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Analysis of data clustering support for service\",\"authors\":\"Y. Zhao, Chi-Hung Chi, Chen Ding\",\"doi\":\"10.1109/ICSESS.2011.5982246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the current direction of service and cloud, unique characteristics of online software services impose new algorithmic requirements and cause differential applicability/ suitability of different clustering approaches in service analytics. In this paper, we investigate the efficiency and effectiveness of current important data clustering techniques, partitioning and hierarchical, for service analytics. It is our goal that results from this paper will serve as requirement guidelines for developing and deploying future intelligence services.\",\"PeriodicalId\":108533,\"journal\":{\"name\":\"2011 IEEE 2nd International Conference on Software Engineering and Service Science\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 2nd International Conference on Software Engineering and Service Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSESS.2011.5982246\",\"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 2nd International Conference on Software Engineering and Service Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSESS.2011.5982246","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With the current direction of service and cloud, unique characteristics of online software services impose new algorithmic requirements and cause differential applicability/ suitability of different clustering approaches in service analytics. In this paper, we investigate the efficiency and effectiveness of current important data clustering techniques, partitioning and hierarchical, for service analytics. It is our goal that results from this paper will serve as requirement guidelines for developing and deploying future intelligence services.