System and method for approximating query results using local and remote neural networks

A non-transitory computer-readable medium storing a set of instructions for improving training of a recurrent neural network (RNN) to provide a response to a table-based database query, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive a plurality of query pairs, each query pair of the plurality of query pairs including a database query and a response, the response generated by executing the database query on a database;
• detect a variable in each query of each query pair
• determine a variance of the variable
• generate a first subset of potential values for the detected variable based on the determined variance, wherein each potential value is different from the response of each query pair
• generate a plurality of training queries, each training query based on a database query of a query pair of the plurality of query pairs and a corresponding potential value from the first subset
• execute each training query to generate a training response;
• and
• train the RNN based on the plurality of training queries and a corresponding training response.

A system for improving training of a recurrent neural network (RNN) to provide a response to a table-based database query comprising:

• a processing circuitry;
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• receive a plurality of query pairs, each query pair of the plurality of query pairs including a database query and a response, the response generated by executing the database query on a database;
• detect a variable in each query of each query pair
• determine a variance of the variable
• generate a first subset of potential values for the detected variable based on the determined variance, wherein each potential value is different from the response of each query pair
• generate a plurality of training queries, each training query based on a database query of a query pair of the plurality of query pairs and a corresponding potential value from the first subset
• execute each training query to generate a training response;
• and
• train the RNN based on the plurality of training queries and a corresponding training response.