{"title":"用于查询处理的快速矩阵乘法","authors":"Xiao Hu","doi":"10.1145/3651599","DOIUrl":null,"url":null,"abstract":"This paper studies how to use fast matrix multiplication to speed up query processing. As observed, computing a two-table join and then projecting away the join attribute is essentially the Boolean matrix multiplication problem, which can be significantly improved with fast matrix multiplication. Moving beyond this basic two-table query, we introduce output-sensitive algorithms for general join-project queries using fast matrix multiplication. These algorithms have achieved a polynomially large improvement over the classic Yannakakis framework. To the best of our knowledge, this is the first theoretical improvement for general acyclic join-project queries since 1981.","PeriodicalId":498157,"journal":{"name":"Proceedings of the ACM on Management of Data","volume":" 45","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast Matrix Multiplication for Query Processing\",\"authors\":\"Xiao Hu\",\"doi\":\"10.1145/3651599\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper studies how to use fast matrix multiplication to speed up query processing. As observed, computing a two-table join and then projecting away the join attribute is essentially the Boolean matrix multiplication problem, which can be significantly improved with fast matrix multiplication. Moving beyond this basic two-table query, we introduce output-sensitive algorithms for general join-project queries using fast matrix multiplication. These algorithms have achieved a polynomially large improvement over the classic Yannakakis framework. To the best of our knowledge, this is the first theoretical improvement for general acyclic join-project queries since 1981.\",\"PeriodicalId\":498157,\"journal\":{\"name\":\"Proceedings of the ACM on Management of Data\",\"volume\":\" 45\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM on Management of Data\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.1145/3651599\",\"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 ACM on Management of Data","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.1145/3651599","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper studies how to use fast matrix multiplication to speed up query processing. As observed, computing a two-table join and then projecting away the join attribute is essentially the Boolean matrix multiplication problem, which can be significantly improved with fast matrix multiplication. Moving beyond this basic two-table query, we introduce output-sensitive algorithms for general join-project queries using fast matrix multiplication. These algorithms have achieved a polynomially large improvement over the classic Yannakakis framework. To the best of our knowledge, this is the first theoretical improvement for general acyclic join-project queries since 1981.