Oracle International Corporation

Saurabh Verma

Lik Wong

Douglas N Surber

Ananth Venkata

Mark Dilman

Wei Ming Hu

Leonid Novak

Jean De Lavarene

Amit Goyal

Justin Hyde

John D Poole

Dylan Wan

Francoise J Lawrence

Balaji Krishnan

Terrel Co Seng

Peter Songwen Xu

Pavan Ananthamurthy

Ilesh Garish

Matthew Bedin

Sadiq Shaik

Vijay Kumar Aggarwal

Joseph Kuttikat

Ramy Yanetz

Mahendra Kumar

Systems and methods for implementing extensible attributes in ETL are disclosed. In some examples, attributes configured at a source file may be extracted from the source file. The extracted attributes can be mapped to a target column of a data warehouse table, and then a dynamic ETL script may be generated. The dynamic script may be executed to move data associated with the attributes to an appropriate new column of the data warehouse.

Extensible attributes for data warehouses

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

In accordance with an embodiment, described herein are systems and methods for automatic generation of business intelligence (BI) data models using data introspection and curation, as may be used, for example, with enterprise resource planning (ERP) or other enterprise computing or data analytics environments. The described approach uses a combination of manually-curated artifacts, and automatic generation of a model through data introspection, of a source data environment, to derive a target BI data model. For example, a pipeline generator framework can evaluate the dimensionality of a transaction type, degenerate attributes, and application measures; and use the output of this process to create an output target model and pipeline or load plan. The systems and methods described herein provide a technical improvement in the building of new subject areas or a BI data model within much shorter periods of time.

System and method for automatic generation of BI models using data introspection and curation

Load Plan Generator (LPG) is a BIAPPS utility for generating ODI load plans based on desired subset of fact tables for loading BIAPPS Data Warehouse. The tool simplifies the configurations process by minimizing the manual steps and configurations and provides a guided list of configurations steps and checklists. The load plan components can include different sets of load plans that will be stitched together by the load plan generator to create one load plan for loading chosen fact groups in the warehouse sourcing from different transaction systems.

Load plan generation

A comparative reporting system provides financial benchmarking and analytics. Integrating public reporting and enterprise software systems, raw financial information from external sources derived from a plurality of reporting sources can be ingested and processed to extract attributes and metrics. The extracted attributes and metrics can be data warehoused together with financial information from the enterprise software system. Analytics can be performed to generate a comparison between the enterprise and the plurality of reporting sources.

XBRL comparative reporting

Load Plan Generator (LPG) is a BIAPPS utility for generating ODI load plans based on desired subset of fact tables for loading BIAPPS Data Warehouse. The tool simplifies the configurations process by minimizing the manual steps and configurations and provides a guided list of configurations steps and checklists. The load plan components are basically different sets of load plans that will be stitched together by the load plan generator to create one load plan for loading chosen fact groups in the warehouse sourcing from different transaction systems.

Techniques that enable data extraction, transfer, and load (ETL) operations to be performed asynchronously over communication networks including a Wide Area Network (WAN).

Techniques for performing ETL over a WAN

Growing community of inventors

Cupertino, CA, United States of America

Saurabh Verma