{"title":"SQL:使用纯SQL的大规模数据库内机器学习","authors":"Umar Syed, Sergei Vassilvitskii","doi":"10.1145/3127479.3132746","DOIUrl":null,"url":null,"abstract":"Many enterprises have migrated their data from an on-site database to a cloud-based database-as-a-service that handles all database-related administrative tasks while providing a simple SQL interface to the end user. Businesses are also increasingly relying on machine learning to understand their customers and develop new products. Given these converging trends, there is a pressing need for database-as-a-service providers to add support for sophisticated machine learning algorithms to the core functionality of their products.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"SQML: large-scale in-database machine learning with pure SQL\",\"authors\":\"Umar Syed, Sergei Vassilvitskii\",\"doi\":\"10.1145/3127479.3132746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many enterprises have migrated their data from an on-site database to a cloud-based database-as-a-service that handles all database-related administrative tasks while providing a simple SQL interface to the end user. Businesses are also increasingly relying on machine learning to understand their customers and develop new products. Given these converging trends, there is a pressing need for database-as-a-service providers to add support for sophisticated machine learning algorithms to the core functionality of their products.\",\"PeriodicalId\":20679,\"journal\":{\"name\":\"Proceedings of the 2017 Symposium on Cloud Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 Symposium on Cloud Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3127479.3132746\",\"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 2017 Symposium on Cloud Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3127479.3132746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SQML: large-scale in-database machine learning with pure SQL
Many enterprises have migrated their data from an on-site database to a cloud-based database-as-a-service that handles all database-related administrative tasks while providing a simple SQL interface to the end user. Businesses are also increasingly relying on machine learning to understand their customers and develop new products. Given these converging trends, there is a pressing need for database-as-a-service providers to add support for sophisticated machine learning algorithms to the core functionality of their products.