ALGORITHMIC LEARNING ENGINE FOR DYNAMICALLY GENERATING PREDICTIVE ANALYTICS FROM HIGH VOLUME, HIGH VELOCITY STREAMING DATA

An algorithmic real-time learning engine comprising an algorithmic model generator configured to process a set of system variables from a big data source 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; statistical test model about correlations, differences between variables, or patterns in time across variables; and recurring clusters model of similar observations across variables. A data preprocessor can select system variables of interest, align the selected system variables based on time, and arrange the aligned variables into rows. The selected system variables can also be aggregated based on a pre-defined aggregate. A visualization processor generates visualizations based on the set of system variables and the predictive model, the statistical test, or recurring cluster.