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 per-spective of the underlying data. Supporting efficient index-ing facilities for multi-dimensional cube queries is an issue of some complexity. In practice, the difficulty of the in-dexing 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 hierar-chy 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 hierar-chical queries.

Aggregation, Caching, Data cubes, Granularity, Hierarchies, Indexing, Materialization, OLAP, Parallelization
8th ACM International Workshop on Data Warehousing and OLAP, DOLAP 2005
Carleton University

Dehne, F, Eavis, T. (Todd), & Rau-Chaplin, A. (Andrew). (2005). Parallel querying of ROLAP cubes in the presence of Hierarchies. Presented at the 8th ACM International Workshop on Data Warehousing and OLAP, DOLAP 2005. doi:10.1145/1097002.1097019