Abstract: A method for aggregating sparse data in a multidimensional array by using a composite join hierarchy created by segmenting the data so that each segment of the hierarchy processed is smaller and more likely to fit in memory. The method employs a recursive sub-cubing mechanism wherein an n-dimensional cube is broken into a number of (n−1)-dimensional cubes and each of those cubes are solved as (n−2)-dimensional cubes etc. Within each division, the processing is segmented by hierarchy level so a dimension with three hierarchy levels (for example, month-quarter-year) would form three separate subcubes with one less dimension. This algorithm produces one ‘worklist’ for every combination of hierarchy levels in the cube. Each of these worklists is represented as a bitmap of the cells contained within it and may be used as a basis of generating more aggregate worklists.

Abstract: In a multi-dimensional database, a composite data construct is created to accommodate sparse dimensions. When the database is defined, sparse dimensions are identified and composites of those sparse dimensions are created. The composites are stored in a linearized array. The amount of storage space allocated is chosen by the database administrator to satisfy reasonable expected growth of the sparse data.