Oracle International Corporation

Numerify, Inc.

Digital.ai Software, Inc.

Renu Chintalapati

Rahul Kapoor

Ananth Venkata

Gaurav Rewari

Jagdish Chand

Florian Schouten

Dirk Reinshagen

Aravind Sridharan

Ravishankar Muniasamy

Ashlesh Bajpai

David Shenk

Srinivas M Vedagiri

Jacques Vigeant

Vikas Agrawal

Rajesh Balu

Manisha Gupta

David Granholm

Naren Chawla

Abha Jain

Matthew Bedin

Vijay Kumar Aggarwal

Vivek Gandhari

Manjunath Rajanna Gangadhar

Stefan Schmitz

Systems and methods for providing autonomous user-directed insights and recommendations are provided herein. For example, a system includes a non-transitory computer-readable medium and a processor communicatively coupled to the non-transitory computer-readable medium. The processor is configured to execute processor-executable instructions to determine, by an insight engine, first usage tracking information associated with a first client device and generate, by the insight engine, a user-directed insight based on the first usage tracking information associated with the first client device. The user-directed insight includes a natural language insight. The processor is also configured to execute processor-executable instructions to generate, by a recommendation engine, recommendations based on the user-directed insight and the first usage tracking information, where each of the recommendations includes a recommendation response and one of a recommendation for a dashboard profile corresponding to the user-directed insight or a recommendation for creating a dashboard corresponding to the user-directed insight.

Autonomous user-directed insights and dashboard recommendations

In accordance with an embodiment, an analytic applications environment 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 environment; and supports the development of computer-executable software analytic applications. A data pipeline or process, such as, for example, an extract, transform, load process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema. The data pipeline or process populates their data warehouse instance and database tables with data as received from their enterprise software application or data environment, as defined by a combination of the analytic applications schema, and their customer schema.

System and method for data analytics with an analytic applications environment

Described herein are techniques for identifying highly relevant content for a user to view in the form of KPI cards and providing the relevant view to the user automatically or by suggestion. The KPIs of highest practical and statistical significance are provided when the user accesses the user interface. In some embodiments, when the user is viewing a KPI, other relevant KPIs may be provided for the user to view as suggestions. Further, in some embodiments, the user may be provided with the KPIs of significance based on anomaly detection, and the explanation for the anomaly as well as suggestions for remedying any issues may be provided to the user. The highly informational content can be surfaced through the use of the advanced machine learning algorithms described herein.

Techniques for data-driven correlation of metrics

A method for automatically determining one or more cross customer metrics from a cross customer store for cross customer analytics is provided. The method includes the steps of: (i) automatically standardizing dimensions associated with the one or more customers to create one or more cross customer metrics; (ii) automatically determining incremental data from one or more customer specific stores by tracking (a) a time stamp of a last pull time (T) for customer data stored in the one or more customer specific stores, and (b) a number of items involved (I); (iii) automatically updating the one or more cross customer metrics in the cross customer store with the incremental data; and (iv) automatically comparing the one or more cross customer metrics of a customer with one or more peer groups created at run time based on customer selected values for one or more parameters.

Efficient cross customer analytics

Approaches for preserving customizations made to a data management system. Metadata that identifies a mapping between data fields of one or more data source schemas to columns of one or more data tier schemas is maintained. Each of the one or more data tier schemas stores data retrieved from the one or more data source schemas. Values from the metadata are read to dynamically generate software processes. The software processes may update one or more existing ETL processes to reflect an updated state of the one or more data tier schemas and may generate any new ETL processes required to retrieve data stored in the one or more data source schemas and load the retrieved data into the one or more data tier schemas. Customizations made to portions the data management system are preserved in an automated fashion when new versions of system components are deployed.

Automated customization preservation

An automated system for defining a star schema for a data source. The system based on automatically gathered information from the data source such as entities and columns, entity column types and lengths, entity keys, relationships between and within entities, measures, workflow and correlated attributes, specialized entities, an update frequency of entities and columns, and grouping of entity and column updates associated with the source database automatically determines facts, dimensions, dimension hierarchies, measures, workflow specific measures (if data source has workflows) and workflow correlated attribute specific measures (if data source has temporal, priority, ownership and progress tracking attributes) to come up with a star schema for the data source.

Automated definition of data warehouse star schemas

An automated system for validating and refining a template warehouse star schema for a customized data source. The system based on automatically gathered information from the data source such as custom entities and constituent columns, data type and length of custom columns, custom entity keys, relationships of custom entities with other entities, relationships within custom entities, contribution of custom entities to specialized entities, update frequency of custom entities and columns including grouping of updates, frequently used expressions on custom columns and contribution of custom entities and columns to workflow and correlated attributes automatically determines facts, dimensions, dimension hierarchies, measures, workflow specific measures (if data source has workflows) and workflow correlated attribute specific measures (if data source has temporal, priority, ownership and progress tracking workflow correlated attributes) to validate and refine the star schema of the template warehouse to accommodate customizations of the data source.

Automated refinement and validation of data warehouse star schemas

Growing community of inventors

San Ramon, CA, United States of America

Renu Chintalapati