RCUBE: Parallel Multi-Dimensional ROLAP Indexing

Frank Dehne, Todd Eavis and Andrew Rau-Chaplin
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Frank Dehne: Carleton University, Canada
Todd Eavis: Concordia University, Canada
Andrew Rau-Chaplin: Dalhousie University, Canada

International Journal of Data Warehousing and Mining (IJDWM), 2008, vol. 4, issue 3, 1-14

Abstract: 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.

Date: 2008
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