Maintaining data cubes under dimension updates

TLDR

OLAP systems model facts as points in a multidimensional space of application‑related dimensions organized hierarchically, but in practice dimensions frequently require updates—such as adding categories or altering hierarchies—that current commercial systems poorly support, leaving materialized aggregate views inadequately maintained. The authors aim to formalize dimension updates in a multidimensional model and define primitive operators for them. They study the impact of these updates on materialized views and present an algorithm for efficient maintenance.

Abstract

OLAP systems support data analysis through a multidimensional data model, according to which data facts are viewed as points in a space of application-related "dimensions", organized into levels which conform to a hierarchy. The usual assumption is that the data points reflect the dynamic aspect of the data warehouse, while dimensions are relatively static. However, in practice, dimension updates are often necessary to adapt the multidimensional database to changing requirements. Structural updates can also take place, like addition of categories or modification of the hierarchical structure. When these updates are performed, the materialized aggregate views that are typically stored in OLAP systems must be efficiently maintained. These updates are poorly supported (or not supported at all) in current commercial systems, and have received little attention in the research literature. We present a formal model of dimension updates in a multidimensional model, a collection of primitive operators to perform them, and a study of the effect of these updates on a class of materialized views, giving an algorithm to efficiently maintain them.