Oracle International Corporation

Ananth Venkata

Jacques Vigeant

Venugopal Surendran

Balaji Krishnan

Vikas Agrawal

Manisha Gupta

Pavan Ananthamurthy

Satish Gopalakrishna

Raghuram Venkatasubramanian

Saugata Chowdhury

Roger Bolsius

Alextair Dominico Mascarenhas

Renu Chintalapati

Reginald A Hansbrough

Narayan Madhavan Nayar

Harvard Pan

Rajesh Balu

Naren Chawla

Wai On Lee

Dustin Bauder

David Granholm

Joseph Kuttikat

Ashish Mittal

John Fuller

Jagdish Chand

Nikhil Surve

Dirk Reinshagen

Matthew Bedin

Malhar Chaudhari

Saurabh Verma

Ashlesh Bajpai

Stefan Schmitz

In accordance with an embodiment, described herein is a system and method for providing support for extensibility and customization in an analytic applications environment. An extract, transform, load (ETL) or other data pipeline or process provided by the analytic applications environment, can operate in accordance with an analytic applications schema and/or a customer schema associated with a customer (tenant), to receive data from the customer's enterprise software application or data environment, for loading into a data warehouse instance. A semantic layer enables the use of custom semantic extensions to extend a semantic model, and provide custom content at a presentation layer. Extension wizards or development environments can guide users in using the custom semantic extensions to extend or customize the semantic model, through a definition of branches and steps, followed by promotion of the extended or customized semantic model to a production environment.

System and method for customization in an analytic applications environment

Systems and method for SaaS/PaaS resource usage and allocation in an analytic applications environment. An exemplary method can provide an analytic applications environment, a control plane comprising a server, the control plane further comprises a provisioning component and a console interface, a data warehouse, and a monitoring agent. The method can provision an instance of the data warehouse in the context of a tenant, the provisioned instance of the data warehouse having an initial size. Upon the provisioning the instance of the data warehouse, the method can add adding an entry to a metrics repository of the monitoring agent, the added entry indicative of the initial size of the provisioned instance of the data warehouse, the added entry being tagged, the tag being indicative of the tenant. The method can monitor, by the monitoring agent, an amount of data stored at the provisioned instance of the data warehouse.

System and method for SAAS/PAAS resource usage and allocation in an analytic applications environment

In accordance with an embodiment, described herein is a system and method for providing extensibility in an analytic applications environment, including a semantic layer that enables the use of custom semantic extensions to extend a semantic data model (semantic model). In accordance with an embodiment, when customizations are made to the semantic model, the system stores changes to the semantic model as an action-set, rather than as a changed-state. This allows the system to replay changes on the factory model, to get back to a desired end state, much as an operating system update does not affect the underling setup.

System and method for semantic model action sets and replay in an analytic applications environment

In accordance with an embodiment, described herein is a system and method for providing extensibility in an analytic applications environment, including a semantic layer that enables the use of custom semantic extensions to extend a semantic data model (semantic model). In accordance with an embodiment, the system enables use of a fragmented query model—when customizations are made to the semantic model, the system can dynamically merge the changes from the various deltas when queries are generated at runtime, to dynamically surface appropriate data based on the extended semantic model.

System and method for use of a fragmented query model in an analytic applications environment

Techniques are disclosed for querying, retrieval, and presentation of data. A data analytic system can enable a user to provide input, through a device to query data. The data analytic system can identify the semantic meaning of the input and perform a query based on the semantic meaning. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query. The data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user.

Techniques for semantic searching

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

A method of identifying causal relationships between time series may include accessing a hierarchy of nodes in a data structure, where each node in the plurality of nodes may include a time series of data. The method may also include identifying a subset of nodes in the plurality of nodes for which causal relationships may exist in the corresponding time series. The method may additionally include generating a model for each of the subset of nodes, where the model may receive the subset of nodes and generate coefficients indicating how strongly each of the subset of nodes causally affects other nodes in the subset of nodes. The method may further include generating a ranked output of nodes that causally affect a first node in the subset of nodes based on an output of the corresponding model.

Identifying regulator and driver signals in data systems

An example system and method for selectively conveying content via User Interface (UI) display screen sections of Business Intelligence (BI) software and for facilitating user interaction therewith, e.g., so as to impart usefulness or preference ratings thereto, which can then affect subsequently displayed content. An example method includes providing one or more UI controls enabling a user to provide an input query and to interact with resulting content (i.e., query results, also called search results), where the user interaction with the content provides information (e.g., context information) indicative of a usefulness or preference rating pertaining to the content; thereby enabling modifications to subsequently retrieved content in accordance with the usefulness or preference rating(s). Such rating(s) may be aggregated from plural users of an associated software application. Additional UI controls may provide options to selectively share relevant retrieved content with other users, such as nearby users that have the appropriate permissions to view the shared content.

Content display and interaction according to estimates of content usefulness

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

A computer-implemented method includes providing a fixed portion of a data model, uploading external files to an extended portion of the data model, matching at least some parts of the external files to the fixed portions of the data model, and using data from both the fixed and extended portions of the data model together. A computer-implemented method includes receiving a query from a user to a data model that includes a stored subject area portion and a stored extended subject area portion, accessing at least one storage to retrieve the subject area portion and the extended subject area portion, joining relevant portions of the subject area and extended subject area, extracting answers to the query from the joined portions, and reporting the answers to the user.

Uploading external files and associating them with existing data models

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 an embodiment, described herein is a system and method for providing extensibility in an analytic applications environment, including a semantic layer that enables the use of custom semantic extensions to extend a semantic data model (semantic model). In accordance with an embodiment, customizations to the out-of-the-box semantic model are performed using a layered approach, wherein the factory code for the semantic model remains intact, with changes/delta editable by the customer layered on top of that model, such that the changes can be patched/reversed if necessary.

Growing community of inventors

San Ramon, CA, United States of America

Ananth Venkata