Parallel Querying of ROLAP Cubes in the Presence of Hierarchies

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 multidimen-sional 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 subdivide 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.