Introduction – slide 8

So, process mining can assist in analyzing all the data that’s generated. And we’ve shown you many examples of processes and software systems that actually support processes. So, the bank cards, the public transport card, websites, they’re all software systems that in some way interact with the world– you, us, and other systems. And they have to know how to interact with this world. Therefore, they’re configured using process models. Process models describe what input can be received and how the software system should react. So, this process model in some sense describes the world and using that knowledge they configure the software system. So, the software system is actually executing a process. When this happens, I have to do that. In this state I can expect this or that.

Well, during this execution event data is recorded. Every step, every check in, every order, everything is recorded in databases. And this is the data that process mining looks at. And process mining bridges the gap between event logs, event data, and process models. Process mining techniques can be roughly divided in three categories. Discovery– using solely the event data, we can discover a process model that describes how the software system for instance is behaving. Secondly, we can do conformance checking. Using the added discover process model or a process model that was actually used to configure the system we can check using the data if the system or the users of the system comply with what the process model describes. Finally, once we have a process model, again either discovered or provided, we can enhance this. Since we can relate the data to the process model, we can predict timing information on top of this. Where are people waiting? Which path is most frequently executed? But also how are users in the system collaborating? Processes mining can provide answers to all these questions.