PnP: Parallel and External Memory Iceberg Cube Computation

Chen, Y.; Dehne, Frank; Eavis, Todd; Rau-Chaplin, Andrew

doi:10.1109/icde.2005.107

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Various methods have been proposed for building ROLAP datacubes (Agrawal et al, 1996;Beyer & Ramakrishnan, 1999;Chen, Dehne, Eavis, Green Tau-Chaplin, & Sithirasenan, 2006;Chen, Dehne, Eavis, & Rau-Chaplin, 2004;Chen, Dehne, Eavis, & Rau-Chaplin, 2005Gray et al, 1996;Harinarayan et al, 1996;Ross & Srivastava, 1997;Sarawagi, Agrawal, & Gupta, 1996), but there are only very few results available for the indexing of such cubes. For sequential query processing, Gupta, Harinarayan, and Rajaraman (1997) propose an indexing model composed of a collection of b-trees.…”

Section: Distributed Index Construction For Rolapmentioning

confidence: 99%

Rcube

Dehne

Eavis

Rau-Chaplin

2008

International Journal of Data Warehousing and Mining

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This article addresses the query performance issue for Relational OLAP (ROLAP) datacubes. We present RCUBE, a distributed multidimensional ROLAP indexing scheme which is practical to implement, requires only a small communication volume, and is fully adapted to distributed disks. Our solution is efficient for spatial searches in high dimensions and scalable in terms of data sizes, dimensions, and number of processors. Our method is also incrementally maintainable. Using “surrogate” group-bys, it allows for the efficient processing of arbitrary OLAP queries on partial cubes, where not all of the group-bys have been materialized. Our experiments with RCUBE show that the ROLAP advantage of better scalability, in comparison to MOLAP, can be maintained while providing a fast and flexible index for OLAP queries.

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