{"title":"走向自修复的web服务组合","authors":"Guoquan Wu, Jun Wei, Tao Huang","doi":"10.1145/1640206.1640221","DOIUrl":null,"url":null,"abstract":"To achieve self-healing web services composition, much work has been studied in the area of web services composition recently. However, most work addresses the problem of runtime monitoring, diagnosis and recovery in isolation. What is missing, however, is a unified solution that can be used to tackle this challenge in a principled manner. This paper presents a fresh view on self-healing web services composition. In particular, rather than building baseline system model a priori, we advocate using statistical learning theory(SLT) technique to extract it by observing the behavior of web services composition and locate the potential anomaly.","PeriodicalId":20631,"journal":{"name":"Proceedings of the 8th Asia-Pacific Symposium on Internetware","volume":"192 1","pages":"15"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Towards self-healing web services composition\",\"authors\":\"Guoquan Wu, Jun Wei, Tao Huang\",\"doi\":\"10.1145/1640206.1640221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To achieve self-healing web services composition, much work has been studied in the area of web services composition recently. However, most work addresses the problem of runtime monitoring, diagnosis and recovery in isolation. What is missing, however, is a unified solution that can be used to tackle this challenge in a principled manner. This paper presents a fresh view on self-healing web services composition. In particular, rather than building baseline system model a priori, we advocate using statistical learning theory(SLT) technique to extract it by observing the behavior of web services composition and locate the potential anomaly.\",\"PeriodicalId\":20631,\"journal\":{\"name\":\"Proceedings of the 8th Asia-Pacific Symposium on Internetware\",\"volume\":\"192 1\",\"pages\":\"15\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th Asia-Pacific Symposium on Internetware\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1640206.1640221\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th Asia-Pacific Symposium on Internetware","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1640206.1640221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
To achieve self-healing web services composition, much work has been studied in the area of web services composition recently. However, most work addresses the problem of runtime monitoring, diagnosis and recovery in isolation. What is missing, however, is a unified solution that can be used to tackle this challenge in a principled manner. This paper presents a fresh view on self-healing web services composition. In particular, rather than building baseline system model a priori, we advocate using statistical learning theory(SLT) technique to extract it by observing the behavior of web services composition and locate the potential anomaly.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
