I/O-efficient statistical computing with RIOT

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 computin...

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Vydáno v:2010 IEEE 26th International Conference on Data Engineering (ICDE 2010) s. 1157 - 1160
Hlavní autoři: Yi Zhang, Weiping Zhang, Jun Yang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.03.2010
Témata:
Adaptive arrays
Business
Computer science
Data analysis
Database systems
Libraries
MATLAB
Multidimensional systems
Spatial databases
Switches
ISBN:142445445X, 9781424454457
ISSN:1063-6382
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Abstract 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.
AbstractList 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.
Author Weiping Zhang
Jun Yang
Yi Zhang
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  surname: Jun Yang
  fullname: Jun Yang
  email: junyang@cs.duke.edu
  organization: Dept. of Comput. Sci., Duke Univ., Durham, NC, USA
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SubjectTerms Adaptive arrays
Business
Computer science
Data analysis
Database systems
Libraries
MATLAB
Multidimensional systems
Spatial databases
Switches
Title I/O-efficient statistical computing with RIOT
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