Systems and methods for natural language querying

1. A data visualization system, comprising:

a processor; and a memory, the memory comprising: a core grammar library, comprising a list of regular expression—system function pairs, wherein:

each regular expression—system function pair comprises a regular expression and a system function; and the system function is associated with a set of one or more parameters and can be performed on a database; and

a natural language query (NLQ) application; where the NLQ application configures the processor to: obtain a database;

generate a Pearson's correlation coefficient (PCC) matrix from the obtained database by calculating a correlation coefficient between all pairs of numeric dimensions and assembling the correlation coefficient values into a square matrix;

generate a plurality of suggested NLQs such that suggested NLQs that involve a dimension of the obtained database most highly correlated with all other dimensions of the obtained database, wherein generating the suggested NLQs based on the PCC matrix;

present the plurality of suggested NLQs to a user;

obtain an NLQ directed at the obtained database from the user;

parse the obtained NLQ using the core grammar library to identify the system function of a regular expression—system function pair of the list of regular expression—system function pairs and the set of one or more parameters associated with the system function; and perform the identified system function using the set of one or more parameters to visualize at least a portion of the obtained database.

2. The system of claim 1 , wherein the statistical analysis comprises calculating Shannon Entropy for each dimension of the obtained database.

3. The system of claim 1 , wherein a suggested NLQ in the plurality of suggested NLQs performs a smart mapping.

4. The system of claim 1 , wherein: the NLQ application further configures the processor to obtain an initial NLQ; the initial NLQ comprises a suffix-operator; and the obtained NLQ is the suggested NLQ in the plurality of suggested NLQs that most closely resembles the initial NLQ.

5. The system of claim 1 , wherein the memory further contains an extended grammar library comprising a second list of regular expression—system function pairs that are domain-specific to the obtained database, where the extended grammar library is used with the core grammar library to parse the obtained NLQ.

6. The system of claim 1 , wherein the NLQ application further directs the processor to:

determine if the obtained NLQ is fully parameterized; and

request correction if the obtained NLQ is not fully parameterized.

7. The system of claim 1 , where:

the NLQ application further configures the processor to:

obtain an initial NLQ;

in response to an inability to parse the initial NLQ, identify key words in the initial NLQ if it is not able to be parsed using the core grammar library; and

generate a plurality of suggested NLQs based on the key words; and

the NLQ application configures the processor to obtain an NLQ by further configuring the processor to:

provide the suggested NLQs;

receive a selection of one of the suggested NLQs; and

resolve unspecified parameters if present in the selected suggested NLQ.

8. The system of claim 1 , wherein the obtained NLQ directed at the database is selected from the plurality of suggested NLQs.

9. The system of claim 1 , wherein the NLQ application further configures the processor to visualize the at least a portion of the database in a three-dimensional virtual environment.

10. A method for natural language querying a data visualization system, comprising:

obtaining a database;

generating a Pearson's correlation coefficient (PCC) matrix from the obtained database by calculating a correlation coefficient between all pairs of numeric dimensions and assembling the correlation coefficient values into a square matrix;

generating a plurality of suggested NLQs such that suggested NLQs that involve a dimension of the obtained database most highly correlated with all other dimensions of the obtained database, wherein generating the suggested NLQs based on the PCC matrix;

presenting the plurality of suggested NLQs to a user via a display;

obtaining a natural language query (NLQ) directed at the obtained database from the user;

parsing the obtained NLQ using a core grammar library to identify a system function and a set of one or more parameters, where:

the core grammar library comprises a list of regular expression—system function pairs; and

the system function is associated with the set of one or more parameters and can be performed on a database;

performing the identified system function using the set of one or more parameters to visualize at least a portion of the obtained database using the display.

11. The method of claim 10 , wherein the statistical analysis comprises calculating Shannon Entropy for each dimension of the obtained database.

12. The method of claim 10 , wherein a suggested NLQ in the plurality of suggested NLQs performs a smart mapping.

13. The method of claim 10 , wherein: the method further comprises obtaining an initial NLQ; the initial NLQ comprises a suffix-operator; and the obtained NLQ is the suggested NLQ in the plurality of suggested NLQs that most closely resembles the initial NLQ.

14. The method of claim 10 , further comprising using an extended grammar library with the core grammar library to parse the obtained NLQ, where the extended grammar library comprises a second list of regular expression—system function pairs that are domain-specific to the obtained database.

15. The method of claim 10 , further comprising:

determining if the obtained NLQ is fully parameterized; and

requesting correction if the obtained NLQ is not fully parameterized.

16. The method of claim 10 , where:

the method further comprises:

obtaining an initial NLQ;

in response to an inability to parse the NLQ, identifying key words in the initial NLQ if it is not able to be parsed using the core grammar library; and

generating a plurality of suggested NLQs based on the key words; and

obtaining an NLQ further comprises:

providing the suggested NLQs;

receiving a selection of one of the suggested NLQs; and

resolving unspecified parameters if present in the selected suggested NLQ.

17. The method of claim 10 , wherein the obtained NLQ directed at the database is selected from the plurality of suggested NLQs.

18. The method of claim 10 , wherein the NLQ application further configures the processor to visualize the at least a portion of the database in a three-dimensional virtual environment.