Abstract:
The data cube operator exemplifies two of the most important aspects of OLAP queries: aggregation and dimension hierarchies. In earlier work we presented Dwarf, a highly compressed and clustered structure for creating, storing and indexing data cubes. Dwarf is a complete architecture that supports queries and updates, while also including a tunable granularity parameter that controls the amount of materialization performed. However, it does not directly support dimension hierarchies. Rollup and drilldown queries on dimension hierarchies that naturally arise in OLAP need to be handled externally and are, thus, very costly. In this paper we present extensions to the Dwarf architecture for incorporating rollup data cubes, i.e. cubes with hierarchical dimensions. We show that the extended Hierarchical Dwarf retains all its advantages both in terms of creation time and space while being able to directly and efficiently support aggregate queries on every level of a dimension's hierarchy.
