Online Analytical Processing (OLAP) is a database paradigm that supports the rich analysis of multi-dimensional data. OLAP is often supported by a logical structure known as the Cube, a data model that provides an intuitive array-based perspective of the underlying data. However, supporting efficient OLAP query resolution in enterprise scale environments is an issue of considerable complexity. In practice, the difficulty of the problem is exacerbated by the existence of dimension hierarchies that sub-divide core dimensions into aggregation layers of varying granularity. Common hierarchy-sensitive query operations such as Rollup and Drilldown can be very costly. Moreover, facilities for the representation of more complex hierarchical relationships are not well supported by conventional techniques.  This thesis presents a robust hierarchy and caching framework that supports the efficient and transparent manipulation of attribute hierarchies within relational environments. Experimental results show that compared to the current methods, very little additional overhead is introduced by the proposed framework, even when advanced functionality is exploited