A. Cabrera, Clayton J. Faber, Kyle Cepeda, Robert Derber, Cooper Epstein, Jason Zheng, R. Cytron, R. Chamberlain
{"title":"DIBS:数据集成基准套件","authors":"A. Cabrera, Clayton J. Faber, Kyle Cepeda, Robert Derber, Cooper Epstein, Jason Zheng, R. Cytron, R. Chamberlain","doi":"10.1145/3185768.3186307","DOIUrl":null,"url":null,"abstract":"As the generation of data becomes more prolific, the amount of time and resources necessary to perform analyses on these data increases. What is less understood, however, is the data preprocessing steps that must be applied before any meaningful analysis can begin. This problem of taking data in some initial form and transforming it into a desired one is known as data integration. Here, we introduce the Data Integration Benchmarking Suite (DIBS), a suite of applications that are representative of data integration workloads across many disciplines. We apply a comprehensive characterization to these applications to better understand the general behavior of data integration tasks. As a result of our benchmark suite and characterization methods, we offer insight regarding data integration tasks that will guide other researchers designing solutions in this area.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"2010 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"DIBS: A Data Integration Benchmark Suite\",\"authors\":\"A. Cabrera, Clayton J. Faber, Kyle Cepeda, Robert Derber, Cooper Epstein, Jason Zheng, R. Cytron, R. Chamberlain\",\"doi\":\"10.1145/3185768.3186307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the generation of data becomes more prolific, the amount of time and resources necessary to perform analyses on these data increases. What is less understood, however, is the data preprocessing steps that must be applied before any meaningful analysis can begin. This problem of taking data in some initial form and transforming it into a desired one is known as data integration. Here, we introduce the Data Integration Benchmarking Suite (DIBS), a suite of applications that are representative of data integration workloads across many disciplines. We apply a comprehensive characterization to these applications to better understand the general behavior of data integration tasks. As a result of our benchmark suite and characterization methods, we offer insight regarding data integration tasks that will guide other researchers designing solutions in this area.\",\"PeriodicalId\":10596,\"journal\":{\"name\":\"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering\",\"volume\":\"2010 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3185768.3186307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3185768.3186307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
As the generation of data becomes more prolific, the amount of time and resources necessary to perform analyses on these data increases. What is less understood, however, is the data preprocessing steps that must be applied before any meaningful analysis can begin. This problem of taking data in some initial form and transforming it into a desired one is known as data integration. Here, we introduce the Data Integration Benchmarking Suite (DIBS), a suite of applications that are representative of data integration workloads across many disciplines. We apply a comprehensive characterization to these applications to better understand the general behavior of data integration tasks. As a result of our benchmark suite and characterization methods, we offer insight regarding data integration tasks that will guide other researchers designing solutions in this area.