Oracle International Corporation

Alextair Dominico Mascarenhas

Saugata Chowdhury

Raghuram Venkatasubramanian

Ashish Mittal

Roger Bolsius

Samar Lotia

Harvard Pan

Victor Belyaev

Alvin Raj

Gabby Rubin

John Fuller

Ananth Venkata

Jacques Vigeant

Venugopal Surendran

Steve Simon Joseph Fernandez

Kenneth Eng

Kenneth Khiaw Hong Eng

Vijayakumar Ranganathan

Samir Satpathy

Praful Hebbar

Arun Santhanam

Harvard Jun-Hua Pan

Ladislav Kovac

Mahadevan Rajagopalan

Yi Ge

David Wong

In accordance with an embodiment, described herein is a system and method for providing query acceleration with a computing environment such as, for example, a business intelligence environment, database, data warehouse, or other type of environment that supports data analytics. A middle layer is provided as a long-term table data storage format; and one more acceleration formats, or acceleration tables, can be periodically regenerated from the middle layer, wherein a determination can be made as to whether an accelerated table exists for a dataset table, and if so, then the accelerated table is used to process the query.

System and method for query acceleration for use with data analytics environments

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

An administration tool can be used to provide data flexibility in a business intelligence (BI) server that is associated with a data warehouse. The administration tool can display one or more data objects that are adapted to be imported from an application framework into a physical model maintained on the BI server. The administration tool can further map the one or more data objects in the physical model into a logical model, also maintained on the BI server, which corresponds to at least one target table in the data warehouse. Additionally, the administration tool can publish an extension input to an extender associated with the data warehouse based on the logical model, wherein the extender operates to generate one or more metadata extensions based on the extension input.

System and method for providing data flexibility in a business intelligence server using an administration tool

An input extension specification can be generated, via an administration tool on a business intelligence (BI) server, for an extender associated with a data warehouse in order to extend the data warehouse. The administration tool can request for data flow information of the data warehouse from the extender. The data flow information indicates the mapping relationship between one or more source data objects and one or more target tables in the data warehouse. The administration tool further allows a user to select said one or more target tables in the data warehouse to be extended based on the data flow information, and builds the input extension specification for the extender.

System and method for specifying metadata extension input for extending data warehouse

Systems and methods are described for automating the creation of data marts within an enterprise. Data is maintained in a plurality of data sources that include at least a relational database and a multidimensional database. The system includes a business intelligence server that provides a virtual logical semantic model to integrate all of the plurality of data sources. The user specifies a list of levels and measures on the virtual logical semantic model. The list of levels and measures can span data from multiple data sources. The user can also specify a location in the plurality of data sources that will store the aggregate matrix. Once the list of levels and measures are specified, the business intelligence server generates a multidimensional cube to store the data for the aggregate matrix and stores the multidimensional cube in the data source location.

Method for automatically creating a data mart by aggregated data extracted from a business intelligence server

Systems and methods are described for performing a SQL bypass in a business intelligence (BI) server environment. The BI server initially receives a SQL query and constructs an extensible markup language (XML) representation of the SQL query. The XML representation is sent to an application server that hosts an application developer framework (ADF). The ADF receives the XML representation of the SQL query and constructs a composite view object at runtime, to access the appropriate data. It then generates a database-specific query based on the composite view object and returns it to the BI server. The BI server replans the database-specific query and sends it to a database storing the data.

System and method for providing a composite view object and SQL bypass in a business intelligence server

An aspect of the present invention simplifies generating a star schema from a snowflake schema. In an embodiment, a user first specifies fact tables to be included in a star schema, and a synchronization tool inspects the snowflake schema to determine the dimension tables linked to the specified fact tables. The determined dimension tables are included in the star schema sought to be generated.

Generation of star schemas from snowflake schemas containing a large number of dimensions

An extender associated with a data warehouse can support metadata extension for the data warehouse. The extender can create an ETL extension plan for extending the data warehouse. The ETL extension plan includes one or more ETL plan elements that indicate extensions on ETL metadata objects. The one or more ETL plan elements within the ETL extension plan can be ordered, and ETL metadata extension can be performed based on the ETL extension plan.

System and method for supporting data warehouse metadata extension using an extender

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Redwood Shores, CA, United States of America

Alextair Dominico Mascarenhas