Process flow behavior prediction using machine learning on aggregated partial process flow information

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause the system to perform operations, the operations comprising:
• obtaining data corresponding to a completed process flow, the completed process flow being associated with an incident that has been resolved;
• generating one or more labels representing characteristics of the incident that serve as target variables for training a machine learning model based on the data corresponding to the completed process flow;
• extracting a subset of the data corresponding to a predefined time range cutoff defined within a total amount of time to resolve the incident;
• generating a plurality of features representing identifiable attributes derived from the obtained data corresponding to the completed process flow for training the machine learning model based on the subset of the data corresponding to the predefined time range cutoff;
• and
• training the machine learning model to predict future behavior of an ongoing process flow using the plurality of features and the one or more labels associated with the data corresponding to the completed process flow.

One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform operations, the operations comprising:

• obtaining data corresponding to a completed process flow, the completed process flow being associated with an incident that has been resolved;
• generating one or more labels representing characteristics of the incident that serve as target variables for training a machine learning model based on the data corresponding to the completed process flow;
• extracting a subset of the data corresponding to a predefined time range cutoff defined within a total amount of time to resolve the incident;
• generating a plurality of features representing identifiable attributes derived from the obtained data corresponding to the completed process flow for training the machine learning model based on the subset of the data corresponding to the predefined time range cutoff;
• and
• training the machine learning model to predict future behavior of an ongoing process flow using the plurality of features and the one or more labels associated with the data corresponding to the completed process flow.