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I/O-efficient statistical computing with RIOT

Publication ,  Journal Article
Zhang, Y; Zhang, W; Yang, J
Published in: Proceedings - International Conference on Data Engineering
June 1, 2010

Statistical analysis of massive data is becoming indispensable to science, commerce, and society today. Such analysis requires efficient, flexible storage support and special optimization techniques. In this demo, we present RIOT (R with I/O Transparency), a system that extends R, a popular computing environment for statistical data analysis. RIOT makes R programs I/O-efficient in a way transparent to users. It features a flexible array storage manager and an optimization engine suitable for statistical and numerical operations. RIOT also seamlessly integrates with external database systems, offering additional opportunities for processing data that reside in databases by blurring the boundary between database and host-language processing. This demo will show how statistical computation can be effectively and efficiently handled by RIOT. © 2010 IEEE.

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Published In

Proceedings - International Conference on Data Engineering

DOI

ISSN

1084-4627

Publication Date

June 1, 2010

Start / End Page

1157 / 1160
 

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Zhang, Y., Zhang, W., & Yang, J. (2010). I/O-efficient statistical computing with RIOT. Proceedings - International Conference on Data Engineering, 1157–1160. https://doi.org/10.1109/ICDE.2010.5447819
Zhang, Y., W. Zhang, and J. Yang. “I/O-efficient statistical computing with RIOT.” Proceedings - International Conference on Data Engineering, June 1, 2010, 1157–60. https://doi.org/10.1109/ICDE.2010.5447819.
Zhang Y, Zhang W, Yang J. I/O-efficient statistical computing with RIOT. Proceedings - International Conference on Data Engineering. 2010 Jun 1;1157–60.
Zhang, Y., et al. “I/O-efficient statistical computing with RIOT.” Proceedings - International Conference on Data Engineering, June 2010, pp. 1157–60. Scopus, doi:10.1109/ICDE.2010.5447819.
Zhang Y, Zhang W, Yang J. I/O-efficient statistical computing with RIOT. Proceedings - International Conference on Data Engineering. 2010 Jun 1;1157–1160.

Published In

Proceedings - International Conference on Data Engineering

DOI

ISSN

1084-4627

Publication Date

June 1, 2010

Start / End Page

1157 / 1160