Ying Chen, A. Rau-Chaplin, F. Dehne, Todd Eavis, D. Green, E. Sithirasenan
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cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes
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.
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