Distributive and algebraic aggregation computation in multidimensional database systems

Multidimensional aggregation plays an important role in systems that maintain large data sets. A conceptual Multidimensional Aggregation Object (MAO), which consists of measures, scopes and aggregation functions, is introduced to represent relationships among aggregators for the entire addressable data set.; In the MAO model, aggregations of low-level (intermediate) data can be reused for aggregations on high-level data along the same dimension. Efficient caching of intermediate aggregated data is presented to improve performance. Intermediate data need to be synchronized when fact data are updated, so we propose direct and indirect compensating as well as fully recomputing cache-updating approaches for this purpose.; Both caching and maintenance methodologies can be applied to data indexing if the index transformation is a homomorphism of the original data domain. This implies that systems are able to maintain traditionally independently developed index structures as well as the basic input data.; In order for the system to perform according to our methodologies, a meta-data script language called Execution Plans is presented. Experimental results reveal significant performance improvements when using MAO for distributive and algebraic aggregations.; The proposed data aggregation technique can be applied to equip data-warehousing environments with additional integrated computation capabilities. OLAP systems can benefit from being able to prepare well-maintained calculated results. Our MAO model can enhance data mining tasks by providing necessary aggregations as well as organized data indexing. Decision making and scientific computing systems can all take advantage of our versatile aggregations to compute very large data sets.