Root cause analysis based on process optimization data

A non-transitory computer-readable storage medium configured to store instructions that, in response to being executed, causes a system to perform operations, the operations comprising:

• in a processor:
• receiving log data associated with a first trace between a first activity and a second activity of a process, wherein the first trace corresponds to a sequence of operations executed between the first activity and the second activity of the process;
• determining a state of inefficiency between a first user input associated with the first activity and a second user input associated with the second activity based on the received log data, wherein the state of inefficiency corresponds to an additional time period between first user input associated with the first activity and the second user input associated with the second activity;
• applying a first machine learning (ML) model on the received log data based on the determined state of inefficiency;
• determining a first label and a first value to be associated with the first trace of the process based on the application of the first ML model, wherein the first label and the first value indicates a root cause for the determined state of inefficiency associated with at least one task executed between the first activity and the second activity;
• generating presentation data associated with the determined state of inefficiency of the first trace based on the determination of the first label and the first value;
• and
• transmitting the generated presentation data on a user device.

A system, comprising:

• a processor configured to:
• receiving log data associated with a first trace between a first activity and a second activity of a process, wherein the first trace corresponds to a sequence of operations executed between the first activity and the second activity of the process;
• determine a state of inefficiency between a first user input associated with the first activity and a second user input associated with the second activity based on the received log data, wherein the state of inefficiency corresponds to an additional time period between first user input associated with the first activity and the second user input associated with the second activity;
• apply a first machine learning (ML) model on the received log data based on the determined state of inefficiency;
• determine a first label and a first value to be associated with the first trace of the process based on the application of the first ML model, wherein the first label and the first value indicates a root cause for the determined state of inefficiency associated with at least one task executed between the first activity and the second activity;
• generate presentation data associated with the determined state of inefficiency of the first trace based on the determination of the first label and the first value;
• and
• transmit the generated presentation data on a user device.