Oracle International Corporation

Yanicklo Technology Limited Liability Company

Gabby Rubin

Victor Belyaev

Samar Lotia

Alvin Raj

John Fuller

Ashish Mittal

Saugata Chowdhury

Alextair Dominico Mascarenhas

Guy Shaked

Steve Simon Joseph Fernandez

Dima Alberg

Marina Sadetsky

Vladimir Braverman

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 various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

System and method for data visualization using machine learning and automatic insight of facts associated with a set of data

In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

System and method for data visualization using machine learning and automatic insight of outliers associated with a set of data

In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more segments within a data set, associated with a target attribute value, based on, for example, the use of a classification and regression tree and a combination of different driving factors, or same driving factors with different values. Information describing segments associated with the data set can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

System and method for data visualization using machine learning and automatic insight of segments associated with a set of data

A method of effectively representing and processing data sets with time series is disclosed. The method may comprise representing time series as a virtual part of data in a data store layer of a user system, thereby allowing processing of time-series related queries in said data store layer of said user system.

Methods for effective processing of time series

Growing community of inventors

Sunnyvale, CA, United States of America

Gabby Rubin