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22nd International Conference on Data Engineering (ICDE'06)
cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes
Atlanta, Georgia
April 03-April 07
ISBN: 0-7695-2570-9
ASCII Text
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Y. Chen, A. Rau-Chaplin, F. Dehne, T. Eavis, D. Green, E. Sithirasenan, "cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes," Data Engineering, International Conference on, pp. 164, 22nd International Conference on Data Engineering (ICDE'06), 2006.
 
 
BibTex
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@article{ 10.1109/ICDE.2006.32,
author = {Y. Chen and A. Rau-Chaplin and F. Dehne and T. Eavis and D. Green and E. Sithirasenan},
title = {cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes},
journal ={Data Engineering, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2570-9},
pages = {164},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDE.2006.32},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Engineering, International Conference on
TI - cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes
SN - 0-7695-2570-9
SP
EP
A1 - Y. Chen,
A1 - A. Rau-Chaplin,
A1 - F. Dehne,
A1 - T. Eavis,
A1 - D. Green,
A1 - E. Sithirasenan,
PY - 2006
KW - null
VL - 0
JA - Data Engineering, International Conference on
ER -
 
Y. Chen, Dalhousie University, Canada
A. Rau-Chaplin, Dalhousie University, Canada
F. Dehne, Carleton University, Canada
T. Eavis, Concordia University, Canada
D. Green, Griffith University, Australia
E. Sithirasenan, Griffith University, Australia
We present the cgmOLAP server, the first fully functional parallel OLAP system able to build data cubes at a rate of more than 1 Terabyte per hour. cgmOLAP incorporates a variety of novel approaches for the parallel computation of full cubes, partial cubes, and iceberg cubes as well as new parallel cube indexing schemes. The cgmOLAP system consists of an application interface, a parallel query engine, a parallel cube materialization engine, meta data and cost model repositories, and shared server components that provide uniform management of I/O, memory, communications, and disk resources.
Citation:
Y. Chen, A. Rau-Chaplin, F. Dehne, T. Eavis, D. Green, E. Sithirasenan, "cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes," icde, pp.164, 22nd International Conference on Data Engineering (ICDE'06), 2006
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