Augmenting decision making via interactive what-if analysis

1. A method for augmenting decision-making via interactive what-if analysis, the method comprising:

receiving, by a query execution engine, a state specification of a graphical user interface that includes a selection of driver importance variables that correlate to a target variable;

generating, by the query execution engine, a database query using the received state specification, wherein the database query is composed to retrieve a query result from a cloud-based data warehouse;

based on the query result, calculating, by the query execution engine, an importance value of each selected driver importance variable relative to the target variable;

presenting the calculated importance value in a driver importance view of the graphical user interfaces;

calculating the importance values based on a machine learning model;

training the machine learning model to generate the driver importance view, the training comprising:

obtaining training data sets, each training data set of historical data comprising:

one or more values for the target variable; values of driver importance variables associated with the one or more values for the target variable;

training the machine learning model based on the training data sets; and

applying the machine learning model to the selection of one or more driver importance variables to calculate the importance value of each selected driver importance variable.

2. The method of claim 1 , further comprising receiving graphical query inputs through graphical elements of the graphical user interface.

3. The method of claim 1 , wherein the query comprises a SQL query.

4. The method of claim 1 , wherein the graphical user interface is presented in a web browser.

5. The method of claim 1 , wherein the driver importance view comprises a window of a decision analysis view that includes the driver importance view and a dataset corresponding to a plurality of values for the driver importance variables and the target variables.

6. The method of claim 1 , further comprising presenting the driver importance view and the dataset in a same graphical user interface.

7. The method of claim 1 , further comprising presenting the driver importance view and the dataset in a same web browser window.

8. An apparatus for augmenting decision-making via interactive what-if analysis, the apparatus comprising a computer processor, a computer memory operatively coupled to the computer processor, the computer memory having disposed within it computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of:

receiving, by a query execution engine, a state specification of a graphical user interface that includes a selection of driver importance variables that correlate to a target variable;

generating, by the query execution engine, a database query using the received state specification, wherein the database query is composed to retrieve a query result from a cloud-based data warehouse;

based on the query result, calculating, by the query execution engine, an importance value of each selected driver importance variable relative to the target variable;

presenting the calculated importance value in a driver importance view of the graphical user interface;

calculating the importance values based on a machine learning model;

training the machine learning model to generate the driver importance view, the training comprising:

training the machine learning model to generate the driver importance view, the training comprising:

obtaining training data sets, each training data set of historical data comprising:

one or more values for the target variable;

values of driver importance variables associated with the one or more values for the target variable;

training the machine learning model based on the training data sets; and

applying the machine learning model to the selection of one or more driver importance variables.

9. The apparatus of claim 8 , wherein the computer program instructions further cause the apparatus to carry out the steps of receiving graphical query inputs through graphical elements of the graphical user interface.

10. The apparatus of claim 8 , wherein the driver importance view comprises a window of a decision analysis view that includes the driver importance view and a dataset corresponding to a plurality of values for the driver importance variables and the target variables.

11. The apparatus of claim 8 , wherein the computer program instructions further cause the apparatus to carry out the steps of presenting the driver importance view and the dataset in a same graphical user interface.

12. A computer program product for prefetching query results using expanded queries, the computer program product disposed upon a non-transitory computer readable medium, the computer program product comprising computer program instructions that, when executed, cause a computer to carry out the steps of:

receiving, by a query execution engine, a state specification of a graphical user interface that includes a selection of driver importance variables that correlate to a target variable;

generating, by the query execution engine, a database query using the received state specification, wherein the database query is composed to retrieve a query result from a cloud-based data warehouse;

based on the query result, calculating, by the query execution engine, an importance value of each selected driver importance variable relative to the target variable;

presenting the calculated importance value in a driver importance view of the graphical user interface;

calculating the importance values based on a machine learning model;

training the machine learning model to generate the driver importance view, the training comprising:

obtaining training data sets, each training data set of historical data comprising:

one or more values for the target variable;

values of driver importance variables associated with the one or more values for the target variable;

training the machine learning model based on the training data sets; and

applying the machine learning model to the selection of one or more driver importance variables.

13. The computer program product of claim 12 , wherein the computer program instructions further cause the computer to carry out the steps of receiving graphical query inputs through graphical elements of the graphical user interface.

14. The computer program product of claim 12 , wherein the driver importance view comprises a window of a decision analysis view that includes the driver importance view and a dataset corresponding to a plurality of values for the driver importance variables and the target variables.