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RCUBE: Parallel Multi-Dimensional ROLAP Indexing

Author

Listed:
  • Frank Dehne

    (Carleton University, Canada)

  • Todd Eavis

    (Concordia University, Canada)

  • Andrew Rau-Chaplin

    (Dalhousie University, Canada)

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.

Suggested Citation

  • Frank Dehne & Todd Eavis & Andrew Rau-Chaplin, 2008. "RCUBE: Parallel Multi-Dimensional ROLAP Indexing," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 4(3), pages 1-14, July.
    • Handle: RePEc:igg:jdwm00:v:4:y:2008:i:3:p:1-14
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