Oracle International Corporation

Xiaolan Shen

Harish Akali

Andrew Bodge

Luming Han

Ravi Pattabhi

Edwin Meijer

Aravindan Vijayan

Sangeeta Dattawadkar

Techniques are described for prefetching analytic results across multiple levels of a dimension in a multidimensional dataset. In some embodiments, an application layer sends a request to view a summary of a multidimensional dataset, where the request targets a first level of a particular dimension of the multidimensional dataset. In response to receiving the request, a pre-fetch range is determined. The pre-fetch range identifies how many levels to analyze along the particular dimension of the multidimensional dataset. At least one analytical operation is performed for the first level of the particular dimension and one or more levels that are within the pre-fetch range of the first level. A first analytic result for the first level is returned to the application layer. For each respective level of the one or more levels within the pre-fetch range, a respective analytic result is stored in a cache.

Prefetching analytic results across multiple levels of data

A method, system, and computer program product provides the capability to compare data object definitions in a database in a less costly and less time-consuming manner than previous techniques. A method of comparing multiple versions of data item definitions in a database comprises generating a first version of comparison information relating to a plurality of data item definitions in the database by comparing information relating to data item definitions obtained from a first source and information relating to data item definitions obtained from a second source and generating a second version of comparison information relating to a plurality of data item definitions in the database by comparing information relating to data item definitions obtained from a first source and information relating to data item definitions obtained from a second source to the first version of comparison information.

Method and system for comparing and re-comparing data item definitions

Systems, methods, and other embodiments associated with maintaining referential integrity while masking database columns are described. One example method includes determining a transitive closure for dependency relationships involving a column to be masked and other columns related to the column to be masked through a dependency relationship. The example method may also include identifying a root column for a column in the transitive closure and creating a mapping table that stores original values and masking values for the root column. The method includes masking the root column and related child columns based on the mapping table in a manner that maintains referential integrity between the root column and the child column. Integrity may be maintained by masking corresponding values in the root column and a child column(s) with identical mask values from the mapping table.

Maintaining referential integrity while masking

Systems, methodologies, media, and other embodiments associated with producing non-repeating random values for use in data masking are described. One example method includes accessing data describing a mask for a value in a database table column to be masked. The example method may also include identifying a number of distinct values in the database table column to be masked and determining a field width for a mask to provide unique random numbers to mask the distinct values. The method may include identifying locations in a mask value to store the unique random value and locations in a mask value to store filler values. The mask value may include different character sets and may comply with user specified formats. The method may include generating a set of statements that when executed produce a mapping table that includes masks for distinct values in the database table column to be masked.

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Nashua, NH, United States of America

Xiaolan Shen