Methods, apparatus, systems and articles of manufacture for providing query selection systems

A non-transitory computer readable storage medium comprising instructions to cause a machine to at least:

• generate a relational database based on a list of first properties respectively present in one or more objects associated with a graph database;
• train a machine learning model to select one of the graph database or the relational database for processing a query, the selection based on a query performance predicted for the one of the graph database or the relational database, the training of the machine learning model to include:
• determining a first result based on evaluation of a training query using the graph database and the relational database;
• determining a second result based on evaluation of the training query using the machine learning model;
• and
• adjusting weights of the machine learning model based on execution of a cross-entropy loss function, the first result and the second result provided as input to the cross-entropy loss function;
• execute the trained machine learning model based on the query to output a value indicative of a selected one of the graph database or the relational database;
• and
• route the query to a query engine corresponding to the selected one of the graph database or the relational database, the query engine to process the query using the selected one of the graph database or the relational database.

A method to provide query selection, the method comprising:

• generating a relational database based on a list of first properties respectively present in one or more objects associated with a graph database;
• training a machine learning model to select one of the graph database or the relational database for processing a query, the selection based on a query performance predicted for the one of the graph database or the relational database, the training of the machine learning model to include:
• determining a first result based on evaluation of a training query using the graph database and the relational database;
• determining a second result based on evaluation of the training query using the machine learning model;
• and
• adjusting weights of the machine learning model based on execution of a cross-entropy loss function, the first result and the second result provided as input to the cross-entropy loss function;
• executing the trained machine learning model based on the query to output a value indicative of a selected one of the graph database or the relational database;
• and
• routing the query to a query engine corresponding to the selected one of the graph database or the relational database, the query engine to process the query using the selected one of the graph database or the relational database.