{"title":"映像数据库","authors":"Dan Olteanu, Maximilian Schleich","doi":"10.1145/3003665.3003667","DOIUrl":null,"url":null,"abstract":"This paper overviews factorized databases and their application to machine learning. The key observation underlying this work is that state-of-the-art relational query processing entails a high degree of redundancy in the computation and representation of query results. This redundancy can be avoided and is not necessary for subsequent analytics such as learning regression models.","PeriodicalId":21740,"journal":{"name":"SIGMOD Rec.","volume":"18 1","pages":"5-16"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"82","resultStr":"{\"title\":\"Factorized Databases\",\"authors\":\"Dan Olteanu, Maximilian Schleich\",\"doi\":\"10.1145/3003665.3003667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper overviews factorized databases and their application to machine learning. The key observation underlying this work is that state-of-the-art relational query processing entails a high degree of redundancy in the computation and representation of query results. This redundancy can be avoided and is not necessary for subsequent analytics such as learning regression models.\",\"PeriodicalId\":21740,\"journal\":{\"name\":\"SIGMOD Rec.\",\"volume\":\"18 1\",\"pages\":\"5-16\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"82\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIGMOD Rec.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3003665.3003667\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIGMOD Rec.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3003665.3003667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper overviews factorized databases and their application to machine learning. The key observation underlying this work is that state-of-the-art relational query processing entails a high degree of redundancy in the computation and representation of query results. This redundancy can be avoided and is not necessary for subsequent analytics such as learning regression models.
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