Parallel querying of ROLAP cubes in the presence of hierarchies

  • Authors:

  • Frank Dehne;Todd Eavis;Andrew Rau-Chaplin

  • Affiliations:

  • Carleton University, Ottawa, Canada;Concordia University, Montreal, Canada;Dalhousie University, Halifax, Canada

  • Venue:
  • Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
  • Year:
  • 2005

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Abstract

Online Analytical Processing is a powerful framework for the analysis of organizational data. OLAP is often supported by a logical structure known as a data cube, a multidimensional data model that offers an intuitive array-based perspective of the underlying data. Supporting efficient indexing facilities for multi-dimensional cube queries is an issue of some complexity. In practice, the difficulty of the indexing problem is exacerbated by the existence of attribute hierarchies that sub-divide attributes into aggregation layers of varying granularity. In this paper, we present a hierarchy and caching framework that supports the efficient and transparent manipulation of attribute hierarchies within a parallel ROLAP environment. Experimental results verify that, when compared to the non-hierarchical case, very little overhead is required to handle streams of arbitrary hierarchical queries.