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

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2016-06-16Online Publication Date
2016-06-16Print Publication Date
2016Permanent link to this record
http://hdl.handle.net/10754/621289
Metadata
Show full item recordAbstract
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.Citation
Agrawal D, Kruse S, Ouzzani M, Papotti P, Quiane-Ruiz J-A, et al. (2016) Rheem. Proceedings of the 2016 International Conference on Management of Data - SIGMOD ’16. Available: http://dx.doi.org/10.1145/2882903.2899414.Conference/Event name
2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016ae974a485f413a2113503eed53cd6c53
10.1145/2882903.2899414