Oracle International Corporation

John Fuller

Victor Belyaev

Samar Lotia

Alvin Raj

Gabby Rubin

Ashish Mittal

Saugata Chowdhury

Alextair Dominico Mascarenhas

Ananth Venkata

Steve Simon Joseph Fernandez

Rajesh Balu

David Granholm

In accordance with an embodiment, described herein is a system and method for providing key performance indicator (KPI) customization in an analytic applications environment, which enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud or computing environment. The system supports customization derived from multiple layers which, in aggregate, can yield a customized performance metric or KPI object. In accordance with an embodiment, the system supports a user interface with icons that describe original (e.g., out-of-the-box or factory) KPIs and user-modified KPIs. When a user modifies an original KPI object to create a customized KPI, its icon is changed to visibly indicate that the user has modified the KPI. The customized KPI can be used within KPI decks, cards, dashboards, or other types of visualizations; while retaining a lineage to the original KPI object.

System and method for providing a user interface for KPI customization in an analytic applications 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 an embodiment, described herein is a system and method for providing key performance indicator (KPI) customization in an analytic applications environment, which enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud or computing environment. The system supports customization derived from multiple layers which, in aggregate, can yield a customized performance metric or KPI object. In accordance with an embodiment, the system enables creation of a customized KPI, by layering variations of the KPI information on an original (e.g., out-of-the-box or factory) KPI object which are merged at runtime to create the final customized KPI. Each delta-KPI can itself also support multiple, e.g., site/user levels/layers. The approach can be used to provide extensibility for user interface decks/dashboards on which KPI visualization objects, such as decks, cards, dashboards, or other types of visualizations, appear.

System and method for providing layered KPI customization in an analytic applications 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 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.

Growing community of inventors

Chicago, IL, United States of America

John Fuller