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Process monitoring is the concept based onmanagingthe state of a business processesexecuted in anenterprise 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 datametricsof aBusiness ProcessModels (BPM) – a weighted directed acyclic graph represents process model with its heuristics such as complete execution time of process, execution time between steps/activities and the status. Firstly, a set of process models are discovered from the events logs using unsupervised learning techniques andheuristics of different steps of a process are modeled using statisticalmethods.
Both process models 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 ifabnormalities are observed.