Automated Software Process Discovery and Real-Time Monitoring

Abstract

Process monitoring is the concept based on managing the state of a business processes executed in an enterprise software. The named status and execution time are two important parameters to infer the state of a process in real-time. An execution of the process end to end is called as process instance. This paper covers monitoring process instances in real-time based on the data metric generated from event logs, where data metrics of a Business Process Models (BPM) – a Weighted Directed Acyclic Graph (WDAG) represents process model with its heuristics such as complete execution time of process, execution time between steps/activities and the status. Firstly, the process model is discovered either by manually or automatically from the events that are recorded from the logs using unsupervised learning techniques and heuristics of different steps of a process are modeled using statistical methods. Both process model and its heuristics are deployed to help process engineers to understand the path of executions of a business process, its state and performance in real-time. Path of executions involves most successful path, longest and shortest paths, error state if the process instances abruptly ends in between, state of failure etc., On the performance front the range of time taken to complete the process, for each step, most and least time consuming, identifying the steps taking more time than usual etc., Process engineers/users are notified if abnormalities are observed. KEY WORDS Process Mining, Process Monitoring, BPM, PM4PY, Standard Deviation (SD)