Coarse grained parallel on-line analytical processing (OLAP) for data mining

被引：0
|
作者：
Dehne, F [1 ]
Eavis, T
Rau-Chaplin, A
机构：
[1] Carleton Univ, Ottawa, ON K1S 5B6, Canada
[2] Dalhousie Univ, Halifax, NS, Canada
来源：
COMPUTATIONAL SCIENCE -- ICCS 2001, PROCEEDINGS PT 2 | 2001年 / 2074卷
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We study the applicability of coarse grained parallel computing model (CGM) to on-line analytical processing (OLAP) for data mining. We present a general framework for the CGM which allows for the efficient parallelization of existing data cube construction algorithms for OLAP. Experimental data indicate that our approach yield optimal speedup, even when run on a simple processor cluster connected via a standard switch.
引用
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页码：589 / 598
页数：10
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