Oracle International Corporation

Raghuram Venkatasubramanian

Roger Bolsius

Saugata Chowdhury

Alextair Dominico Mascarenhas

Harvard Pan

Ananth Venkata

Jacques Vigeant

Venugopal Surendran

Ling Ni

Donko Donjerkovic

Steve Cave

Joel Turkel

Martin Roth

Scott Feinstein

Anne Murphy

James Hartsing

Henry Verschell

Randall Scott Shoup

Samar Lotia

James Wolf

Ekrem S C Soylemez

Kenneth Eng

Kathryn Nash

Paul Kim

Boris Nikulitsa

Vijay Jain

Bei Yu

Steven Mesropian

Lasya Priya Thilagar

Arun Kumar Kalyaana Sundaram

Daren Race

Herve Mazoyer

Neel Madhav

Shashi Anand Babu

Jeffrey Douglas

Arun Santhanam

The present disclosure relates generally to an integrated machine learning platform. The machine learning platform can convert machine learning models with different schemas into machine learning models that share a common schema, organize the machine learning models into model groups based on certain criteria, and perform pre-deployment evaluation of the machine learning models. The machine learning models in a model group can be evaluated or used individually or as a group. The machine learning platform can be used to deploy a model group and a selector in a production environment, and the selector may learn to dynamically select the model(s) from the model group in the production environment in different contexts or for different input data, based on a score determined using certain scoring metrics, such as certain business goals.

Machine learning platform for dynamic model selection

Herein are techniques for automatically leveraging metadata of an analytic view to accelerate a relational query. In an embodiment, a computer stores model metadata that defines an analytic view that contains a join operation that is based on a dimension column of a dimension table and a join column of a fact table. The analytic view also contains a measure that is based on an aggregation operation and a measure column of the fact table. Also stored is denormalization metadata that defines a transparency view that is based on the analytic view. In operation, a query that references the transparency view is received. The query does not reference the analytic view. The query that references the transparency view is executed based on: a) the denormalization metadata that defines the transparency view, b) the model metadata that defines the analytic view, and c) the measure that is based on the aggregation operation and the measure column of the fact table.

Method for generating views based on a semantic model, that allows for autonomous performance improvements and complex calculations

Herein, a computer stores metadata that defines an analytic view that is based on organizing operation(s) that respectively specify organizing column(s) of organizing table(s), including a join operation that specifies a dimension column of a dimension table and a join column of a fact table. An original query is received that does not reference the analytic view. The original query specifies at least one organizing operation that is identical, somewhat similar, or compatible to an organizing operation on which the analytic view is based. Based on that at least one organizing operation that the original query specifies and the metadata that defines the particular analytic view, the original query is determined to satisfy rewrite criterion(s). In response to determining that the original query satisfies the rewrite criteria, a rewritten query that references the analytic view is generated based on the original query that does not reference the analytic view.

Method for using a sematic model to transform SQL against a relational table to enable performance improvements

A business intelligence (BI) server is described that supports data and schemas stored in multiple languages. The BI server implements a lookup table and lookup function that allows users to work with queries in different languages. When the user logs in, a session object is created for the user, which maintains the state information. A session variable specifies the language currently being used by the user. The BI server can inspect this session variable to determine the language of the user and perform the lookup translations as necessary. For example, if the language used by the session is different from the language of the base table storing the necessary information, the BI server can perform a translation by invoking a lookup function. The execution of the lookup can include performing a join operation of the base table with the lookup table to yield a translated value requested by the query.

Systems and methods for providing multilingual support for data used with a business intelligence server

A computer implemented algorithm performs introspection of an uploaded denormalized table and identifies candidate fact and dimension tables. The cardinality values of columns in a candidate dimension table are analyzed to identify simple/complex primary key candidates. Unused columns are further analyzed for assignment to candidate fact and/or dimension tables.

BI cloud services data modeling denormalized table introspection algorithm

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

A business intelligence (BI) server and repository are described which support a set of customized and/or calculated data members. In accordance with an embodiment, the BI server maintains a connection to a plurality of data sources which may store a set of dimension members. The data source can be relational, file storage based, multidimensional and other types. In accordance with an embodiment, the BI server can accept queries from the user that contain calculated members as a parameter. The calculated member is defined by an expression including multiple dimension members and one or more arithmetic operators. The BI server can parse and validate the query and rewrite the query for the data source. After the query is rewritten and optimized, it is executed against the data source and a set of results is received.

Systems and methods for providing custom or calculated data members in queries of a business intelligence server

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

A business intelligence (BI) server and repository are described which support a set of hierarchical relationships among the data. The BI server receives user input specifying a set of parent-child or other ancestral relationship among a set of data in a data source. The BI server generates a set of SQL queries and executes the queries to pre-populate a set of tables which specify the parent child relationships among the data in the data source. One such table is a parent-child relationship closure table that defines the inter-member relationships among the data members. Once the tables are populated, the BI server uses the closure tables to answer queries that require knowledge of the ancestral relationships among data.

Systems and methods for providing value hierarchies, ragged hierarchies and skip-level hierarchies in a business intelligence server

A query generator for generating a query that returns a result set comprising data retrieved from a database and data returned by an analytic function that operates on at least a portion of the retrieved data is disclosed. The result set is arranged as a table having a plurality of rows, and the query generator is adapted to:

Query generator

A method for analyzing data from a database using an analytic database function includes receiving a selection of measured items from a user, receiving a placement item from the user, and determining a partitioning of the selection of measured items from the placement item. A placement item can be a column, a row, or an axis. A template associated with the analytic database function is adapted to define at least one partitioning relative to the placement item. The template is further adapted to define an ordering parameter for the analytic database function and optionally an aggregation level for the analytic database function. A database query is created with the partitioning. The database query can be an SQL statement.

Layout aware calculations

A query generator for generating a query which retrieves a desired set of data from a relational database and performs one or more aggregation functions on the set of data is disclosed. The query generator is adapted to:

Growing community of inventors

Cupertino, CA, United States of America

Raghuram Venkatasubramanian