Abstract: Methods, computer systems, and stored instructions are described herein for densely grouping dimensional data and/or aggregating data using a data structure, such as one that is constructed based on dimensional data. When smaller tables are joined with a larger table, a server may analyze the smaller tables first to determine actual value combinations that occur in the smaller tables, and these actual value combinations are used to more efficiently process the larger table. A dense data structure may be generated by processing dimensional data before processing data from fact table. The dense data structure may be generated by compressing ranges of values that are possible in dimensions into a range of values that actually occurs in the dimensions. The compressed range of values may be represented by dense set identifiers rather than the actual compressed range of values.

Abstract: 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.

Abstract: Methods, computer systems, and stored instructions are described herein for densely grouping dimensional data and/or aggregating data using a data structure, such as one that is constructed based on dimensional data. When smaller tables are joined with a larger table, a server may analyze the smaller tables first to determine actual value combinations that occur in the smaller tables, and these actual value combinations are used to more efficiently process the larger table. A dense data structure may be generated by processing dimensional data before processing data from fact table. The dense data structure may be generated by compressing ranges of values that are possible in dimensions into a range of values that actually occurs in the dimensions. The compressed range of values may be represented by dense set identifiers rather than the actual compressed range of values.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In a multi-threaded computing environment, a shared cache system reduces the amount of redundant information stored in memory. A cache memory area provides both global readable data and private writable data to processing threads. A particular processing thread accesses data by first checking its private views of modified data and then its global views of read-only data. Uncached data is read into a cache buffer for global access. If write access is required by the processing thread, the data is copied into a new cache buffer, which is assigned to the processing thread's private view. The particular shared cache system supports generational views of data. The system is particularly useful in on-line analytical processing of multi-dimensional databases. In one embodiment, a dedicated collector reclaims cache memory blocks for the processing threads. By utilizing a dedicated collector thread, any processing penalty encountered during the reclamation process is absorbed by the dedicated collector.

Abstract: 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.

Abstract: In a multi-threaded computing environment, a shared cache system reduces the amount of redundant information stored in memory. A cache memory area provides both global readable data and private writable data to processing threads. A particular processing thread accesses data by first checking its private views of modified data and then its global views of read-only data. Uncached data is read into a cache buffer for global access. If write access is required by the processing thread, the data is copied into a new cache buffer, which is assigned to the processing thread's private view. The particular shared cache system supports generational views of data. The system is particularly useful in on-line analytical processing of multi-dimensional databases. In one embodiment, a dedicated collector reclaims cache memory blocks for the processing threads. By utilizing a dedicated collector thread, any processing penalty encountered during the reclamation process is absorbed by the dedicated collector.

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-threaded computing environment, a shared cache system reduces the amount of redundant information stored in memory. A cache memory area provides both global readable data and private writable data to processing threads. A particular processing thread accesses data by first checking its private views of modified data and then its global views of read-only data. Uncached data is read into a cache buffer for global access. If write access is required by the processing thread, the data is copied into a new cache buffer, which is assigned to the processing thread's private view. The particular shared cache system supports generational views of data. The system is particularly useful in on-line analytical processing of multi-dimensional databases. In one embodiment, a dedicated collector reclaims cache memory blocks for the processing threads. By utilizing a dedicated collector thread, any processing penalty encountered during the reclamation process is absorbed by the dedicated collector.

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. To minimize the need for input-output data transfers, all the derived worklists of a single worklist are generated at the same time.