Oracle International Corporation

Grant E Hicks

Albert A Hopeman

Caleb Welton

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&minus;1)-dimensional cubes and each of those cubes are solved as (n&minus;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. To minimize the need for input-output data transfers, all the derived worklists of a single worklist are generated at the same time. This is accomplished without keeping more than n-worklists active at any given time to reduce the number of input-output data transfers needed without requiring substantially larger memory space.

Methods and apparatus for aggregating sparse data

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.

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Acton, MA, United States of America

Grant E Hicks