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.

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Persistent URL dx.doi.org/10.1007/3-540-45718-6_64
Citation
Dehne, F, Eavis, T. (Todd), & Rau-Chaplin, A. (Andrew). (2001). Coarse grained parallel on-line analytical processing (OLAP) for data mining. doi:10.1007/3-540-45718-6_64