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Author, Editor

Author(s):

Das, S.
Sismanis, Y.
Beyer, K. S.
Gemulla, Rainer
Haas, P. J.
McPherson, J.

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Not MPG Author(s):

Das, S.
Sismanis, Y.
Beyer, K. S.
Haas, P. J.
McPherson, J.

Editor(s):





BibTeX cite key*:

das10

Title, Booktitle

Title*:

Ricardo: Integrating R and Hadoop


sigmod696-sudipto.pdf (341.46 KB)

Booktitle*:

SIGMOD '10 : Proceedings of the 2010 International Conference on Management of Data

Event, URLs

URL of the conference:

http://www.sigmod2010.org

URL for downloading the paper:

http://doi.acm.org/10.1145/1807167.1807275

Event Address*:

Indianapolis, Indiana, USA

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

6 June 2010

Event End Date:

11 June 2010

Publisher

Name*:

ACM

URL:


Address*:

New York, NY

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

987-998

Year*:

2010

VG Wort Pages:


ISBN/ISSN:

978-1-4503-0032-2

Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

Many modern enterprises are collecting data at the most detailed level
possible, creating data repositories ranging from terabytes to petabytes in
size. The ability to apply sophisticated statistical analysis methods to
this data is becoming essential for marketplace competitiveness. This need
to perform deep analysis over huge data repositories poses a significant
challenge to existing statistical software and data management systems. On
the one hand, statistical software provides rich functionality for data
analysis and modeling, but can handle only limited amounts of data; e.g.,
popular packages like R and SPSS operate entirely in main memory. On the
other hand, data management systems---such as MapReduce-based systems---can
scale to petabytes of data, but provide insufficient analytical
functionality. We report our experiences in building \RICARDO, a scalable
platform for deep analytics. \RICARDO\ is part of the eXtreme Analytics
Platform (XAP) project at the IBM Almaden Research Center, and rests on a
decomposition of data-analysis algorithms into parts executed by the R
statistical analysis system and parts handled by the Hadoop data management
system. This decomposition attempts to minimize the transfer of data across
system boundaries. \RICARDO\ contrasts with previous approaches, which try
to get along with only one type of system, and allows analysts to work on
huge datasets from within a popular, well supported, and powerful analysis
environment. Because our approach avoids the need to re-implement either
statistical or data-management functionality, it can be used to solve
complex problems right now.


Personal Comments:

kein MPI-Autor

Download
Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Databases and Information Systems Group

Audience:

Expert

Appearance:

MPII WWW Server, MPII FTP Server, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{das10,
AUTHOR = {Das, S. and Sismanis, Y. and Beyer, K. S. and Gemulla, Rainer and Haas, P. J. and McPherson, J.},
TITLE = {Ricardo: Integrating {R} and {Hadoop}},
BOOKTITLE = {SIGMOD '10 : Proceedings of the 2010 International Conference on Management of Data},
PUBLISHER = {ACM},
YEAR = {2010},
PAGES = {987--998},
ADDRESS = {Indianapolis, Indiana, USA},
ISBN = {978-1-4503-0032-2},
}


Entry last modified by Klaus Berberich, 03/12/2013
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Editor(s)
[Library]
Created
03/08/2011 01:20:52 PM
Revisions
2.
1.
0.

Editor(s)
Klaus Berberich
Anja Becker
Rainer Gemulla

Edit Dates
03/12/2013 02:15:36 PM
09.03.2011 13:09:54
03/08/2011 01:20:52 PM

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