Oracle International Corporation

Manish Daga

Shivaranjan Gurunanjappa

Prasad Kulkarni

Sudhakara Reddy

Anju Christudas

Munish Kaushik

Anagha A Kadoo

Nirvi Badyal

Suresh Babu Thavarool Puthiyadath

Systems, methods, and other embodiments associated with integrating a machine learning model into a target application are described. In one embodiment, a method includes parsing a definition file that represents the selected ML model to (1) identify one or more input features defined for the ML model, and (2) identify one or more output predictions that the ML model is configured to generate. The input features and output predictions are mapped to locations within the target application. An executable script is generated and configured to function as a data controller or a data communication interface to obtain the input values from the target application and pass data between the target application and the ML model. In response to obtaining the output predictions, the output predictions are inserted in one or more result locations within the target application.

Integrating machine learning models in multidimensional applications

Systems and methods are provided for generating new insights for multi-dimensional data. A plurality of insight definitions that define insights within multi-dimensional data can be received, where the multi-dimensional data include dimensions organized in a hierarchical structure, and the insight definitions include members across the hierarchical dimensions that define a slice of the multi-dimensional data. Descriptive data about a candidate insight defined by at least one of the insight definitions that corresponds to a candidate slice of the multi-dimensional data can be received. A new insight definition based on the candidate insight can be generated, where the new insight definition corresponds to a new slice of the multi-dimensional data, and the new slice of the multi-dimensional data is proximate to the candidate slice of the multi-dimensional data with respect to the hierarchical structure.

Insights for multi-dimensional planning data

Planning granularities can be stored for data elements including a first granularity for a first data combination, the first data combination including a combination of multiple dimensions of data. A comparison metric can be calculated between planned data at the first granularity for the first data combination and actual data observed for the first data combination, wherein the observed data is stored at a different granularity than the planned data. Based on the calculated comparison, the planning granularity for the first data combination can be adjusted from the first granularity to a second granularity. A selection of a cross-section of data spanning multiple dimensions can be received, the selection including the first data combination, wherein a user interface is configured to display data for the first data element at the second granularity based on the adjusting.

Auto-granularity for multi-dimensional data

Embodiments can receive data granularities for data elements, wherein a weekly granularity is received for a first data element and a monthly granularity is received for a second data element. A selection of a cross-section of data can be received spanning a plurality of dimensions in the multi-dimensional data model, the selection including a first data combination based on the first data element and a second data combination based on the second data element. The weekly data of the first data element can be mapped to monthly data according to a predetermined conversion calculation. In a form, monthly data can be displayed for the second data combination based on the second data element and monthly data for the first data combination based on the mapped monthly data.

Mixed data granularities for multi-dimensional data

A system and method for facilitating construction of and/or adaptation of a dynamic software model. One embodiment provides for generating software models by mapping user selections to one or more model features as specified by feature definitions. An initial software model is used to obtain the user selections. Artifacts are associated with the business planning model according to the selections by mapping the selections to model features according to previously determined feature definitions.

Generation of dynamic software models using input mapping with feature definitions

A system and method for facilitating construction of and/or adaptation of a dynamic software model. One embodiment provides for generating software models by mapping user selections to one or more model features as specified by feature definitions. An initial software model is used to obtain the user selections. Artifacts are associated with the initial business planning model according to the selections by mapping the selections to model features according to previously determined feature definitions.

A computer-implemented method can include creating a sandbox responsive to a sandbox creation request from a user, wherein the sandbox represents an alternate version of a base version and is accessible only by the user. The method can also include visually presenting to the user information pertaining to the sandbox and information pertaining to the base version, saving changes to the information pertaining to the sandbox, and publishing the sandbox responsive to a publish request from the user.

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Bengaluru, India

Manish Daga