Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A business intelligence (BI) server maintains a plurality of metadata objects to support the extract, transform and load (ETL) processes. These metadata objects includes a transparent view object, which takes a joined set of source tables and represents a data shape of the joined set of source tables using a transformation, and a ETL mapping association object that maps the transformation contained in the transparent view object to a target table. The BI server can then orchestrate the movement of data from source systems into the target data warehouses in a source and target system agnostic way.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.