Query Processing with Machine Learning

A device-implemented method, comprising:

• receiving an inference query for a database having a database relational engine and an integral machine learning runtime;
• performing static analysis of the inference query to generate a graph representing the inference query with one or more relational database operators and one or more machine learning operators;
• performing cross-optimization of the graph based, at least in part, upon the one or more relational database operators and the one or more machine learning operators;
• and
• generating code for the database based at least in part on the cross-optimization,
• wherein, when executed, the code produces a result of the inference query based at least in part on the one or more relational operators and the one or more machine learning operators.

A system, comprising:

• a processor;
• and
• a computer storage media having computer-executable instructions stored thereupon which, when executed by the processor, cause the processor to:
• receive an inference query for a database having a database relational engine and an integral machine learning runtime;
• perform static analysis of the inference query to generate a graph representing the inference query with one or more relational database operators and one or more machine learning operators;
• perform cross-optimization of the graph based, at least in part, upon the one or more relational database operators and the one or more machine learning operators;
• and
• generate code for the database based at least in part on the cross-optimization,
• wherein, when executed, the code produces a result of the inference query based at least in part on the one or more relational operators and the one or more machine learning operators.

A computer storage media storing computer-readable instructions which, when executed by a processor, cause the processor to perform acts comprising:

• receiving an inference query for a database having a database relational engine and an integral machine learning runtime;
• performing static analysis of the inference query to generate a graph representing the inference query with one or more relational database operators and one or more machine learning operators;
• performing cross-optimization of the graph based, at least in part, upon the one or more relational database operators and the one or more machine learning operators;
• and
• generating code for the database based at least in part on the cross-optimization,
• wherein, when executed, the code produces a result of the inference query based at least in part on the one or more relational operators and the one or more machine learning operators.