Abstract: A system provides multidimensional tuple Hilbert ordering within a linear storage structure to enable more consistent and efficient access to stored multidimensional tuples. The database system comprises a tuple storage module that receives multidimensional data elements, where each multidimensional data element is defined by a plurality of different object types, and where each object is associated with a different dimension within a multidimensional data space. The tuple storage module further forms a respective tuple for each of the multidimensional data elements by mapping each object to an associated reference and applies a Hilbert function to the references of each of the tuples to determine a respective Hilbert ordering for each of the tuples. The system further comprises a linear data storage structure that includes a B-tree for storing the Hilbert ordering.

Abstract: Techniques are described of applying on-line analytical processing (OLAP) to associative databases. For instance, a database adapter implemented in computer hardware or software may automatically create dimensions in a multidimensional dataset for data types in an associative database. The database adapter may then query the associative database for metadata that describes index values of each dimension of the multidimensional dataset. The database adapter may then query the associative database for data values to populate individual cells of the multidimensional database.

Abstract: An enterprise business intelligence system includes a data access service that provides consistent availability of functionality for querying multidimensional data sources regardless of the capabilities of the underlying data sources. The data access service disassembles a multidimensional query into execution units, and may optimize the multidimensional query such that individual execution units may be executed locally or remotely to achieve increase computational efficiently.

Abstract: Techniques are described for compressing multidimensional datasets. For instance, a compression module obtains lists of indices of data along dimensions of the multidimensional dataset. The compression module calculates lists of offset values from the lists of coordinates. The compression module then performs a statistical redundancy data compression algorithm on the lists of offset values. Because the lists of offset values may contain more repetitious values than the initial lists of coordinates, a compression ratio on the multidimensional dataset may be higher than without first calculating the lists of offset values.

Abstract: An enterprise business intelligence system includes a data access service that provides consistent availability of functionality for querying multidimensional data sources regardless of the capabilities of the underlying data sources. The data access service disassembles a multidimensional query into execution units, and may optimize the multidimensional query such that individual execution units may be executed locally or remotely to achieve increase computational efficiently.

Abstract: A system provides multidimensional tuple Hilbert ordering within a linear storage structure to enable more consistent and efficient access to stored multidimensional tuples. The database system comprises a tuple storage module that receives multidimensional data elements, where each multidimensional data element is defined by a plurality of different object types, and where each object is associated with a different dimension within a multidimensional data space. The tuple storage module further forms a respective tuple for each of the multidimensional data elements by mapping each object to an associated reference and applies a Hilbert function to the references of each of the tuples to determine a respective Hilbert ordering for each of the tuples. The system further comprises a linear data storage structure that includes a B-tree for storing the Hilbert ordering.

Abstract: Techniques are described of applying on-line analytical processing (OLAP) to associative databases. For instance, a database adapter implemented in computer hardware or software may automatically create dimensions in a multidimensional dataset for data types in an associative database. The database adapter may then query the associative database for metadata that describes index values of each dimension of the multidimensional dataset. The database adapter may then query the associative database for data values to populate individual cells of the multidimensional database.

Abstract: Techniques are described for compressing multidimensional datasets. For instance, a compression module obtains lists of indices of data along dimensions of the multidimensional dataset. The compression module calculates lists of offset values from the lists of coordinates. The compression module then performs a statistical redundancy data compression algorithm on the lists of offset values. Because the lists of offset values may contain more repetitious values than the initial lists of coordinates, a compression ratio on the multidimensional dataset may be higher than without first calculating the lists of offset values.