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Abstract: On-line Analytical Processing (OLAP) has become one of the most powerful and prominent technologies for knowledge discovery in VLDB (Very Large Database) environments. Central to the OLAP paradigm is the data cube, a multi-dimensional hierarchy of aggregate values that provides a rich analytical model for decision support. Various sequential algorithms for the efficient generation of the data cube have appeared in the literature. However, given the size of contemporary data warehousing... (Update)
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3.7: The cgmCUBE Project: Optimizing Parallel Data Cube.. - Dehne, Eavis, al. (Correct)
0.9: Parallel ROLAP Data Cube Construction on Shared-Nothing.. - Chen, Dehne, Al. (2004) (Correct)
0.8: Improved Data Partitioning for Building Large ROLAP Data.. - Chen, Dehne, Eavis, al. (Correct)
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BibTeX entry: (Update)
@misc{ dehne-cgmcube,
author = "Frank Dehne and Todd Eavis and Andrew Rau-Chaplin",
title = "The cgmCUBE project: Optimizing parallel data cube generation for ROLAP",
url = "citeseer.ist.psu.edu/dehne06cgmcube.html" }
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http://www.olapreport.com
http://www.humanfactor.com/pthreads
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