Rheem: Enabling Multi-Platform Task Execution

Divy Agrawal, Sebastian Kruse, Mourad Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiane-Ruiz, Nan Tang, Mohammed J. Zaki, Lamine Ba, Laure Berti-Equille, Sanjay Chawla, Ahmed Elmagarmid, Hossam Hammady, Yasser Idris, Zoi Kaoudi, Zuhair Khayyat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations


Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases Rheem, 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 Rheem by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion. © 2016 ACM.
Original languageEnglish (US)
Title of host publicationProceedings of the 2016 International Conference on Management of Data - SIGMOD '16
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Print)9781450335317
StatePublished - Jun 16 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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