Algorithmic learning engine for dynamically generating predictive analytics from high volume, high velocity streaming data

A method for processing high volume, high velocity streaming data received from a system process, the method comprising:

• processing a set of system variables from the streaming data using at least one of a pattern recognition algorithm and a statistical test algorithm to identify patterns, relationships between variables, and important variables;
• and
• generating at least one of:
• a predictive model based on the identified patterns, relationships between variables, and important variables;
• a statistical test model about correlations, differences between variables, or patterns in time across variables;
• and
• a recurring clusters model of similar observations across variables.

A system for processing high volume, high velocity streaming data received from a system process, the system comprising:

• a plurality of system process servers configured to:
• generate the streaming high volume, high velocity data;
• a data preprocessor configured to:
• create the set of system variables by performing at least one of aggregating select system variables and aligning select system variables;
• an algorithmic model generator configured to:
• process a set of system variables from the streaming data using at least one of a pattern recognition algorithm and a statistical test algorithm to identify patterns, relationships between variables, and important variables;
• and
• generate at least one of:
• a predictive model based on the identified patterns, relationships between variables, and important variables;
• a statistical test model about correlations, differences between variables, or patterns in time across variables;
• and
• a recurring clusters model of similar observations across variables.