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Improved Data Partitioning for Building Large ROLAP Data Cubes in Parallel  (Make Corrections)  
Ying Chen, Frank Dehne, Todd Eavis, et al.



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Abstract: The pre-computation of data cubes is critical to improving the response time of On-Line Analytical Processing (OLAP) systems and can be instrumental in accelerating data mining tasks in large data warehouses. However, as the size of data warehouses grows, the time it takes to perform this pre-computation becomes a significant performance bottleneck. (Update)

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2.2:   Improved Data Partitioning for Building Large ROLAP Data.. - Chen, Dehne, Eavis, al.   (Correct)
1.5:   Parallel ROLAP Data Cube Construction on Shared-Nothing.. - Chen, Dehne, Al. (2004)   (Correct)
0.8:   The cgmCUBE project: Optimizing parallel data cube.. - Dehne, Eavis.. (2006)   (Correct)

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BibTeX entry:   (Update)

@misc{ chen-improved,
  author = "Ying Chen and Frank Dehne and Todd Eavis and et al.",
  title = "Improved Data Partitioning for Building Large ROLAP Data Cubes in Parallel",
  url = "citeseer.ist.psu.edu/760047.html" }
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