Oracle International Corporation

Caleb Welton

Ekrem Soylemez

Albert A Hopeman

Matthew Dombroski

Albert A Hopeman, Iv

James E Carey

Gregory Dorman

Christopher Kearney

Grant E Hicks

Lev Margulis

Andrew H Goldberg

A method and data structure is described that builds summary information using processor time that is usually proportional to the size of input data and a depth of hierarchies for a plurality of attributes. The output of computation is stored in a smaller area by eliminating redundant storage and computation. An index is generated which includes tuples or rows that include lower bound values for each of the attributes, values of children of the lower bound values based on the hierarchies of the attributes, and coverage class indicators of the coverage classes of the children.

Method and apparatus for storage and retrieval of information in compressed cubes

Predicate abduction involves identifying, from a database statement, predicates on attributes of dimensional data, and applying those predicates to corresponding measure data so that only measure values are fetched and returned for dimension values that satisfy the predicate. In contrast to processing joins of relational data, the subset of the measure data is determined without needing to join a fact table with a dimension table and, therefore, without processing every fact record to determine which measure values are associated with records that satisfy the condition. Buffer reuse involves storing in buffers the 'domains' of queries executing on multidimensional data. Responsive to detecting that execution of a database statement requires multiple iterations of extracting the same multidimensional data, the data is extracted during the first iteration and stored in a buffer so that, for each subsequent iteration, the buffer-stored data is accessed rather than re-extracting the data.

Efficient processing of relational joins of multidimensional data

In processing a query on multidimensional data in a multidimensional schema, a multidimensional database server performs various processes to limit the amount of data that is extracted from the database and presented to a relational database server in response to the request. A subset of data is identified to extract from the multidimensional data, based on the query. In various embodiments, cell-filtering, measure-filtering, and column-filtering criteria identified from the query are used to filter the subset of data, in order to return a minimized set of multidimensional data values from the subset of data. Therefore, computational resources are conserved with respect to further processing by the relational database server in response to the query.

Efficient SQL access to multidimensional data

In a multidimensional database, an aggregation operation is performed in an optimal manner by storing the values included in the/aggregation operation on the same disk page. A sparsity manager determines aggregate values that are computed from other data values during the aggregation operation. Each aggregate value is associated with one or more data values that are used during the aggregation operation to compute the aggregate value. The sparsity manager stores the associated data values in proximity to each other, such as on the same disk page, so that multiple disk page fetches may not be required for the same set of data values during the aggregation operation. The data values used in the aggregation operation can therefore be fetched once from a disk page, and thereafter are found in memory, such as on a cache page corresponding to the disk page. In this manner, multiple fetches for the same disk page during the aggregation operation are avoided.

Multidimensional database storage and retrieval system

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.

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Malden, MA, United States of America

Caleb Welton