{"title":"健壮的查询处理","authors":"G. Graefe","doi":"10.1109/ICDE.2011.5767961","DOIUrl":null,"url":null,"abstract":"In the context of data management, robustness is usually associated with resilience against failure, recovery, redundancy, disaster preparedness, etc. Robust query processing, on the other hand, is about robustness of performance and of scalability. It is more than progress reporting or predictability. A system that fails predictably or obviously performs poorly may be better than an unpredictable one, but it is not robust.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Robust query processing\",\"authors\":\"G. Graefe\",\"doi\":\"10.1109/ICDE.2011.5767961\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the context of data management, robustness is usually associated with resilience against failure, recovery, redundancy, disaster preparedness, etc. Robust query processing, on the other hand, is about robustness of performance and of scalability. It is more than progress reporting or predictability. A system that fails predictably or obviously performs poorly may be better than an unpredictable one, but it is not robust.\",\"PeriodicalId\":332374,\"journal\":{\"name\":\"2011 IEEE 27th International Conference on Data Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 27th International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2011.5767961\",\"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 27th International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2011.5767961","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In the context of data management, robustness is usually associated with resilience against failure, recovery, redundancy, disaster preparedness, etc. Robust query processing, on the other hand, is about robustness of performance and of scalability. It is more than progress reporting or predictability. A system that fails predictably or obviously performs poorly may be better than an unpredictable one, but it is not robust.