Welcome to Week 2!
In the previous week we laid the foundations for this week: we installed the ProM tool and learned what event data and event logs are. We also discussed ways to visualize the event data to start analysing its contents. We also showed how the event data can be filtered.
In this week we discuss the core feature of process mining: discovery of a process model from the (raw) event data.
A process model (for example in the Petri net formalization) describes how activities are related. For instance, the process starts by ‘receive request’, followed by either ‘decline’ or ‘ investigate further’. This is much like the loan application example shown in the previous week.
But how can algorithms discover these process models from raw event data? And which algorithm should I use (since there are several)? And how can I evaluate the resulting process model? You’ll know the answers by the end of this week!