Process Mining is about extrapolating existing knowledge from the event logs which are commonly available in the companies’ information systems. That is a set of analytical techniques that allows the extraction of process descriptions from a set of real executions. These executions are stored in the called event logs. The event logs record events in the execution of tasks. According to the Process Mining Manifesto, “the idea of process mining is to discover, monitor and improve real processes by extracting knowledge from event logs collected by information systems. Process mining includes automated process discovery (i.e., extracting process models from an event log), conformance checking (i.e., monitoring deviations by comparing model and log), and several process enhancement activities, such as social network/organizational mining, automated construction of simulation models, model extension, model repair, case prediction and history-based recommendations”.
Most of the companies create business processes that most of the time, are difficult to control and comprehend. Understanding these processes is fundamental when improvement actions must be taken. The process mining techniques make easier and faster the comprehension of business processes and consequently, become easier control, manage these and take improvement decisions like cost, time and risks reduction.
This is possible thanks to the infinite amount of data companies have in their information systems. Is possible to know which activities are performed, when and by whom. With process mining is so possible to detect or diagnose problems based on facts and not on intuitions. This thanks to the confrontation between the observed behavior and process models, which can be hand-made or automatically discovered. This can help organizations capture information from enterprise transaction systems and provides detailed and data-driven information about how processes are performing.
Process Mining depicts a data-driven view of the process performance helping in visualize business problems and opportunities.
This recent technique, bridges so the gap between traditional model-based process analysis and data-centric analysis techniques such as machine learning and data mining. Process Mining can be applied to any type of operational processes, both for organizations and systems. All the application has in common just that the dynamic behavior needs to be related to process models.