Abstract: In accordance with an embodiment, described herein is a system and method for loading and transforming data to a cloud database, using a hybrid set-based extract, load, transform approach. During the loading and transforming of large amounts of data, from one or more data sources, a data synchronization application sends small batches of data, in parallel streams, to the database, which operates as a transformation engine to load the data. Each stream can be associated with a separate staging table in the database. For each staging table, the subset of data therein is transformed and loaded, before the next subset is processed. A transform and merge process operating at the database can then be used to transform and merge the data, from each of the staging tables, to the target table.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for providing a universal format driven data transformation. In conventional ETL operations, the transform process that transforms data from the source format to the destination format is based on a transformation program. Conventionally, transform logic is developed around the chosen transform program. In accordance with an embodiment, a universal, format-driven data transform can be achieved through the use of a data transformation format. In accordance with an embodiment, a method for providing a universal format driven data transformation can include providing a transformation format in a file, wherein the transformation format is in a human-readable form; and providing, in the transformation format, keywords defining a target table type, a dataset list, and dataset operations to be performed on a dataset from the dataset list.

Abstract: Systems and methods for providing a universal format driven data transformation. In conventional ETL operations, the transform process that transforms data from the source format to the destination format is based on a transformation program. Conventionally, transform logic is developed around the chosen transform program. In accordance with an embodiment, a universal, format-driven data transform can be achieved through the use of a data transformation format. In accordance with an embodiment, a method for providing a universal format driven data transformation can include providing a transformation format in a file, wherein the transformation format is in a human-readable form; and providing, in the transformation format, keywords defining a target table type, a dataset list, and dataset operations to be performed on a dataset from the dataset list.

Abstract: In accordance with an embodiment, described herein is a system and method for loading and transforming data to a cloud database, using a hybrid set-based extract, load, transform approach. During the loading and transforming of large amounts of data, from one or more data sources, a data synchronization application sends small batches of data, in parallel streams, to the database, which operates as a transformation engine to load the data. Each stream can be associated with a separate staging table in the database. For each staging table, the subset of data therein is transformed and loaded, before the next subset is processed. A transform and merge process operating at the database can then be used to transform and merge the data, from each of the staging tables, to the target table.

Abstract: 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.

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: In accordance with an embodiment, described herein is a system and method for loading and transforming data to a cloud database, using a hybrid set-based extract, load, transform approach. During the loading and transforming of large amounts of data, from one or more data sources, a data synchronization application sends small batches of data, in parallel streams, to the database, which operates as a transformation engine to load the data. Each stream can be associated with a separate staging table in the database. For each staging table, the subset of data therein is transformed and loaded, before the next subset is processed. A transform and merge process operating at the database can then be used to transform and merge the data, from each of the staging tables, to the target table.

Abstract: 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.

Abstract: 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.

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: 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.

Abstract: Systems and methods for providing a universal format driven data transformation. In conventional ETL operations, the transform process that transforms data from the source format to the destination format is based on a transformation program. Conventionally, transform logic is developed around the chosen transform program. In accordance with an embodiment, a universal, format-driven data transform can be achieved through the use of a data transformation format. In accordance with an embodiment, a method for providing a universal format driven data transformation can include providing a transformation format in a file, wherein the transformation format is in a human-readable form; and providing, in the transformation format, keywords defining a target table type, a dataset list, and dataset operations to be performed on a dataset from the dataset list.

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: 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.

Abstract: 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.

Abstract: In accordance with an embodiment, described herein is a system and method for loading and transforming data to a cloud database, using a hybrid set-based extract, load, transform approach. During the loading and transforming of large amounts of data, from one or more data sources, a data synchronization application sends small batches of data, in parallel streams, to the database, which operates as a transformation engine to load the data. Each stream can be associated with a separate staging table in the database. For each staging table, the subset of data therein is transformed and loaded, before the next subset is processed. A transform and merge process operating at the database can then be used to transform and merge the data, from each of the staging tables, to the target table.

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: 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.