Displaying query results using machine learning model-determined query results visualizations

1. A non-transitory computer-readable data storage medium storing program code executable by a processor to perform processing comprising:

receiving a query comprising a plurality of sub-queries separated by logical operators, each sub-query comprising a field name and a data value interrelated to one another by a relational operator;

converting the query into a structured query tokenization such that that the data value of each sub-query is replaced by a data type of the data value and the relational operator of each sub-query is removed;

determining one or more query results visualizations for the query by applying a supervised machine learning model to the structured query tokenization to improve how query results of the query are visualized, each query results visualization comprising a graphical representation of which the query results of the query are displayed using graphical elements;

generating the query results of the query by executing the query without using the structured query tokenization into which the query has been converted such that the structured query tokenization is used just for determining the query results visualizations; and

displaying query results of the query using the determined query results visualizations.

2. The non-transitory computer-readable data storage medium of claim 1 , wherein the structured query tokenization cannot be used for executing the query.

3. The non-transitory computer-readable data storage medium of claim 1 , wherein the field name of each sub-query within the query is retained within the structured query tokenization.

4. The non-transitory computer-readable data storage medium of claim 1 , wherein the logical operators separating the sub-queries of the query are retained within the structured query tokenization,

and wherein the structured query tokenization includes functions and/or calculated expressions not used for query evaluation.

5. The non-transitory computer-readable data storage medium of claim 1 , wherein converting the query into the structured query tokenization comprises:

dividing the query into the sub-queries;

retaining the field name of each sub-query;

replacing the data value of each sub-query with the data type of the data value; and

removing the relational operator of each sub-query.

6. The non-transitory computer-readable data storage medium of claim 5 , wherein converting the query into the structured query tokenization further comprises:

retaining the logical operators.

7. The non-transitory computer-readable data storage medium of claim 1 , wherein the processing further comprises:

calculating a prediction score for the query as to each query results visualization with respect to the structured query tokenization; and

culling the query results visualizations based on the calculated prediction scores of the query results visualizations.

8. The non-transitory computer-readable data storage medium of claim 7 , wherein culling the query results visualizations based on the calculated prediction scores of the query results visualizations comprises one or more of:

retaining only the query results visualizations having the calculated prediction scores that are greater than a threshold;

retaining only a threshold number of the query results visualizations having the calculated prediction scores that are highest.

9. The non-transitory computer-readable data storage medium of claim 1 , wherein the processing further comprises:

displaying the graphical representation of each query results visualization; and

receiving a selected graphical representation,

wherein displaying the query results of the query using the determined query results visualizations comprises displaying the query results of the query using the determined query results visualization having the selected graphical representation.

10. A method comprising:

training, by a processor, a supervised machine learning model using a training data set comprising a plurality of training data pairs that each comprise a training structured query tokenization for a corresponding training query and a training query results visualization for the corresponding training query; and

displaying, by the processor, query results of a query using a query results visualization determined for the query by the trained supervised machine learning model,

wherein the query results visualization improves how the query results of the query are visualized and comprises a graphical representation of which the query results of the query are displayed using graphical elements,

wherein the training query of each training data pair comprises a plurality of sub-queries separated by logical operators, each sub-query comprising a field name and a data value interrelated to one another by a relational operator,

and wherein in the training structured query tokenization of each training data pair, the data value of each sub-query of the corresponding training query is replaced by a data type of the data value and the relational operator of each sub-query of the corresponding training query is removed.

11. The method of claim 10 , further comprising:

testing, by the processor, the trained supervised machine learning model using a testing data set comprising a plurality of testing data pairs that each comprise a testing structured query tokenization of a testing query and a testing query results visualization for the testing query.

12. The method of claim 11 , further comprising:

collecting, by the processor, data pairs that each comprise a query and a query results visualization for the query;

converting, by the processor, the query of each data pair into a structured query tokenization; and

dividing, by the processor, the data pairs into the training data set and the testing data set,

wherein the data pairs of the testing data set are different than the data pairs of the training data set.

13. The method of claim 10 , further comprising:

continuously retraining, by the processor, the supervised machine learning model.

14. The method of claim 10 , wherein for each training data pair, the training structured query tokenization cannot be used for executing the corresponding training query.

15. The method of claim 10 , wherein for each training data pair, the field name of each sub-query within the corresponding training query is retained within the training structured query tokenization.

16. The method of claim 10 , further comprising:

receiving, by the processor, the query;

converting, by the processor, the query to a structured query tokenization; and

applying, by the processor, the trained supervised machine learning model to the structured query tokenization to determine the query results visualization for the query.

17. The method of claim 10 , further comprising:

converting, by the processor, the corresponding training query for each training data pair into the training structured query tokenization for the corresponding training query.

18. A computing system comprising:

a processor; and

a memory storing instructions executable by the processor to:

receive a query comprising a plurality of sub-queries separated by logical operators, each sub-query comprising a field name and a data value interrelated to one another by a relational operator;

convert the query into a structured query tokenization such that that the data value of each sub-query is replaced by a data type of the data value and the relational operator of each sub-query is removed;

determine a query results visualization for the query by applying a supervised machine learning model to the structured query tokenization, the query results visualization improving how query results of the query are visualized and comprising a graphical representation of which the query results of the query are displayed using graphical elements; and

display query results of the query using the determined query results visualization.

19. The computing system of claim 18 , wherein the instructions are executable by the processor to convert the query into the structured query tokenization by:

dividing the query into the sub-queries;

retaining the field name of each sub-query;

replacing the data value of each sub-query with the data type of the data value; and

removing the relational operator of each sub-query.

20. The computing system of claim 18 , wherein the instructions are executable by the processor to convert the query into the structured query tokenization by further:

retaining the logical operators.