Oracle International Corporation

Dima Alberg

Guy Shaked

Victor Belyaev

Gabby Rubin

David Greenfield

One or more embodiments include operations associated with semantic classification of data columns. The operations may include receiving a set of data elements corresponding to a data column to be semantically classified, applying a machine learning model to the set of data elements to predict a set of candidate semantic types for the set of data elements, selecting a particular semantic type from the set of candidate semantic types based at least in part on a semantic fit score corresponding to the particular semantic type predicted by the machine learning model, and presenting the particular semantic type as a recommended semantic classification for the data column.

Semantically classifying sets of data elements

Techniques are provided for determining the semantic-type of a target column based on 'fingerprints' that are created based on the values in the target column. The fingerprint set for the target column is only generated once, not once per semantic-type. Thus, the target column only needs to be scanned once, and resource usage is minimized. Once generated, the fingerprint set of the column is compared against fingerprint set that corresponds to each semantic-type to generate a “similarity measure”. The semantic-type whose fingerprint set produces the highest similarity measure relative to the target column's fingerprint set is determined to be the semantic-type of the target column.

Method for identifying a sematic type of data contained in a column of a table

In accordance with an embodiment, described herein is a system and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment. A cube schema inference component can successively perform column-wise splits to calculate information gain ratios between each pair of a plurality of columns in the tabular data. A cross correlation matrix can be constructed from the information gain ratios between each pair of columns. The system can determine relationships among the plurality of columns based on the cross correlation matrix; create a hierarchy directed graph to represent the relationships; and further transform the hierarchy directed graph into a cube schema that can be used to create a cube for loading the tabular data, or to map the tabular data into an existing cube in the multidimensional database environment.

System and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment

In accordance with an embodiment, described herein is a system and method for aggregating values through risk dimension hierarchies to create risk models in a multidimensional database. The system can include a cube that stores a plurality of standard risk measures organized into different risk dimension hierarchies, and a pluggable calculation engine executing a plurality of scripts to dynamically operate on any value in the cube. The system can aggregate values through the plurality of risk dimensional hierarchies, and either store an aggregated value against a dimension member, or dynamically calculate the aggregated value on demand. By aggregating values through the risk dimension hierarchies, a plurality of risk models can be built to measure a variety of risks.

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Beer Sheva, Israel

Dima Alberg