D. Agrawal, M. Ba, Laure Berti-Équille, S. Chawla, A. Elmagarmid, Hossam M. Hammady, Yasser Idris, Zoi Kaoudi, Zuhair Khayyat, Sebastian Kruse, M. Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, N. Tang, Mohammed J. Zaki
{"title":"Rheem:启用多平台任务执行","authors":"D. Agrawal, M. Ba, Laure Berti-Équille, S. Chawla, A. Elmagarmid, Hossam M. Hammady, Yasser Idris, Zoi Kaoudi, Zuhair Khayyat, Sebastian Kruse, M. Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, N. Tang, Mohammed J. Zaki","doi":"10.1145/2882903.2899414","DOIUrl":null,"url":null,"abstract":"Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"43","resultStr":"{\"title\":\"Rheem: Enabling Multi-Platform Task Execution\",\"authors\":\"D. Agrawal, M. Ba, Laure Berti-Équille, S. Chawla, A. Elmagarmid, Hossam M. Hammady, Yasser Idris, Zoi Kaoudi, Zuhair Khayyat, Sebastian Kruse, M. Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, N. Tang, Mohammed J. Zaki\",\"doi\":\"10.1145/2882903.2899414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.\",\"PeriodicalId\":20483,\"journal\":{\"name\":\"Proceedings of the 2016 International Conference on Management of Data\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"43\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 International Conference on Management of Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2882903.2899414\",\"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 2016 International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2882903.2899414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
