{"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}
引用次数: 82
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.