In this course we will cover several process mining activities and they cover several phases of a process mining project. So usually you start with the initialisation phase, followed by the analysis iterations, and then you summarize and you implement.For instance, you start with planning. What process are we looking at? What questions do we want answered? And this gives input for the extraction. Depending on the process and the question you want to answer, you need to extract certain data. Once you have the data you can process it. You can filter out particular cases or events to focus further. Using this filtered data, you can do process mining. There are several techniques that can help you and we will discuss the main ones.
Then a very important step comes– evaluation. Given the process mining results, for instance a process model, you have to be able to evaluate how good this process model describes the data. And this gives input in changing, for instance, parameters orgoing back to the data processing phase and applying process mining techniques again. Once, after evaluation, you believe that the results are correct, you can summarize the results and this gives input for process improvement. So based on your summary of the results you can provide concrete process improvements to the process owner.
And in this course we will cover the steps from extraction to summarizing these results and we’ll mainly focus on the different types of process mining techniques there are and how you can evaluate the results. So in the next lectures, we will install the process mining tool ProM which we will use throughout this course to apply whatever we learn on real data immediately. So we hope to see you again in the next lecture soon.