Contextual modeling using application metadata
Provided is a system and method for building context from software applications and applying the context to visual settings in a graphical user interface. In one example, the method may include receiving an identification of actions performed by a user with respect to a user interface of a software application, receiving application metadata of the actions from the software application, the application metadata providing context associated with the actions, training one or more predictive models to predict user interface preferences for the user based on the actions and the application metadata, and storing the one or more trained predictive models via a storage device.
1. A computing system comprising:
a processor configured to
receive an identification of actions of a user with respect to a user interface of a software application,
receive application metadata of the actions of the user from the software application, the application metadata identifying differences between current user interface settings when the actions are performed via the user interface in comparison to default user interface settings of the user interface, and
train one or more predictive models based on a difference between current visual settings of the user interface when the actions are performed with respect to default visual settings of the user interface, wherein the training of the one or predictive models is to predict measures and dimensions for display via a user interface of the user based on the actions and the identified differences included in the application metadata, wherein the measures comprise numerical values and the dimensions comprise attributes used to break-up the measures into smaller numerical values; and
a storage configured to store the one or more trained predictive models.
2. The computing system of claim 1 , wherein the application metadata comprises user interface settings of measures and user interface settings of dimensions being viewed via the user interface.
3. The computing system of claim 1 , wherein the processor is configured to train a first plurality of contextual models which are associated with a plurality of dimensions of data, respectively, which are capable of being viewed via the user interface.
4. The computing system of claim 1 , wherein the processor is configured to train a second plurality of contextual models which are associated with a plurality of measures of data, respectively, which are capable of being viewed via the user interface.
5. The computing system of claim 1 , wherein the processor is further configured to receive a request from the user for a visualization of a type of data, and predict a chart type from among a plurality of chart types for viewing the type of data based on the one or more trained predictive models.
6. The computing system of claim 5 , wherein the processor is further configured to predict a drill-down level of the user interface for viewing hierarchical attributes of the type of data via execution of the one or more trained predictive models.
7. The computing system of claim 5 , wherein the processor is further configured to predict one or more data filters to apply to the type of data for removing unwanted attributes of the type of data via execution of the one or more trained predictive models.
8. A method comprising:
receiving an identification of actions of a user with respect to a user interface of a software application;
receiving application metadata of the actions of the user from the software application, the application metadata identifying differences between current user interface settings when the actions are performed via the user interface in comparison to default user interface settings of the user interface;
training one or more predictive models based on a difference between current visual settings of the user interface when the actions are performed with respect to default visual settings of the user interface, wherein the training of the one or predictive models is to predict measures and dimensions for display via a user interface of the user based on the actions and the identified differences included in the application metadata, wherein the measures comprise numerical values and the dimensions comprise attributes used to break-up the measures into smaller numerical values; and
storing the one or more trained predictive models via a storage device.
9. The method of claim 8 , wherein the application metadata comprises user interface settings of measures and user interface settings of dimensions being viewed via the user interface.
10. The method of claim 8 , wherein the training comprises training a first plurality of contextual models which are associated with a plurality of dimensions of data, respectively, which are capable of being viewed via the user interface.
11. The method of claim 8 , wherein the training comprises training a second plurality of contextual models which are associated with a plurality of measures of data, respectively, which are capable of being viewed via the user interface.
12. The method of claim 8 , further comprising receiving a request from the user for a visualization of a type of data, and predicting a chart type from among a plurality of chart types for viewing the type of data based on a trained predictive model associated with the type of data.
13. The method of claim 12 , wherein the predicting further comprises predicting a drill-down level of the user interface for viewing hierarchical attributes of the type of data.
14. The method of claim 12 , wherein the predicting further comprises predicting one or more data filters to apply to the type of data for removing unwanted attributes of the type of data.
15. A non-transitory computer-readable medium storing program instructions which when executed by a processor cause a computer to perform a method comprising:
receiving an identification of actions of a user with respect to a user interface of a software application;
receiving application metadata of the actions of the user from the software application, the application metadata identifying differences between current user interface settings when the actions are performed via the user interface in comparison to default user interface settings of the user interface;
training one or more predictive models based on a difference between current visual settings of the user interface when the actions are performed with respect to default visual settings of the user interface, wherein the training of the one or predictive models is to predict measures and dimensions for display via a user interface of the user based on the actions and the identified differences included in the application metadata, wherein the measures comprise numerical values and the dimensions comprise attributes used to break-up the measures into smaller numerical values; and
storing the one or more trained predictive models via a storage device.
16. The non-transitory computer-readable medium of claim 15 , wherein the method further comprises receiving a request from the user for a visualization of a type of data, and predicting a chart type from among a plurality of chart types for viewing the type of data based on the one or more trained predictive models.
17. The non-transitory computer-readable medium of claim 15 , wherein the predicting further comprises predicting a drill-down level of the user interface for viewing hierarchical attributes of the type of data.
18. The non-transitory computer-readable medium of claim 15 , wherein the predicting further comprises predicting one or more data filters to apply to the type of data for removing unwanted attributes of the type of data.
19. The computing system of claim 1 , wherein the application metadata comprises differences between current hierarchical drill-down settings for one or more measures and dimensions in comparison to default hierarchical drill-down settings for the one or more measures and dimensions.