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Skip to 0 minutes and 10 secondsHi. And welcome to this lecture, conformance checking in ProM. So within the whole poses mining spectrum, in this week we focus on conformance checking and extension. And in the last lecture, we discussed conformance checking using alignment, where we take the event log data and align it with the process model. And this process model can we discovered but you can also create it manually and put it in and compare it with the event log data. So using this alignments and this conformance checking, you can see where the data deviates from the model. So let's go to ProM and show you how you can do this using real data and real models.

Skip to 0 minutes and 50 secondsSo with ProM open, let's load the loan example process event log. And let's discover a process model using the inductive miner to mine the PetriNet with the inductive miner. And you get this process model, which looks like the one we've shown in the lecture. However in this data, there are some deviations. So let's try and detect them by using this process model with the event log. And what we want to do is replay the log on a PetriNet for conformance. So select this plug-in for now. And press Start. So in this wizard, you have to select how to map the activities in the process model with the events observed in the event log.

Skip to 1 minute and 36 secondsAnd in this case, we have a perfect match. So everything is OK. Now you can do some further settings. But in general, it's OK to just press Next. And here you can even specify how much it would cost to do a move on model or a move on log on certain activities. So you can indicate preference for some deviations over others. Well, let's click Finish. And now we see the same process model again but with color. So let's zoom in a little bit by scrolling the mouse wheel. And what we can see in the legend is that higher frequency activities get a darker blue color. So these activities have been executed more often.

Skip to 2 minutes and 16 secondsAnd for instance, Acceptance has only been executed 20 times. What you also see is that this activity has a red border and a green and purple bar on the bottom. And as is indicated by the legend, this indicates that 90 times the activity was executed synchronously or correctly and 10 times there was a model move. So in 10 out of 100 cases, this activity was not observed in the traces. So no check in the system was made. You can also see this by opening the Element statistics. And then you see indeed 90 times model move or synchronous move and 10 times a model move in 10 traces.

Skip to 2 minutes and 58 secondsUsually you want to get a global number indicating how well the data aligns with the process model and, therefore, you look at the trace fitness. And in this case, this data complies for 99% with this model. So let's do the same thing but now on real data. So let's import the BPI Challenge 2012, only because there's an A and O activities. So when we do this, we can again use the inductive miner to discover a PetriNet.

Skip to 3 minutes and 31 secondsAnd these are the PetriNet we get. And now we can press the Play button, select the BPI Challenge Event Log. And again, do Replay of the event log on the PetriNet for conformance analysis. Again, we have to indicate that these activities in the model relate to these activities in the event log. But we get a perfect mapping. And we keep all the settings as is. And now it is aligning all the traces and all the events on this process model. And now you, again, see the process model with the transitions colored in. They still mean the same thing.

Skip to 4 minutes and 13 secondsAnd you actually see that only this one, this transition has deviations, but now you see also that certain places have different colors, are actually yellow. That means that in this place or in actually in this mocking so that there was a token in both places. In this situation in the process, we have observed certain move on locks. So for instance, 11 time when there was a token in these two places, 11 times we see, oh, canceled. And 35 times we've seen, oh, created. Well, at this point in time, this was not as expected. And so you can click all the places and see what combine markings you have observed, what activities that you did not expect.

Skip to 5 minutes and 0 secondsAnd if we look at the global statistics of this event log, we see that it is a trace fitness off almost 93%. Not that bad but still there are deviations that you might want to inspect. Another way to show deviations is by using inductive visual miner. So let's go back to the Object View, the BPI Challenge Event Log, and then run the Inductive Visual Miner. So let's wait until it finds a model.

Skip to 5 minutes and 35 secondsPut it on the concept name classifier.

Skip to 5 minutes and 40 secondsAnd this is the process model that we get. And it automatically already starts aligning, as you can see in the bottom, and now it's actually already creating the animation. So if we zoom in, we can see how a process is executed. And if we say show paths and deviations, you actually see some red arcs. And these indicates that this part has been skipped three times. And here for instance at this point in time, 200 times an activity that was not expected was observed. And this also helps you indicate where bottlenecks are, although it shows a bit less detail than the alignments that we've shown before.

Skip to 6 minutes and 24 secondsSo using ProM, we can show in several ways how the data aligns or misaligns with the process model.

Skip to 6 minutes and 34 secondsSo now that you've seen in ProM how you can do this, you know how you can validate where the data deviates from your process model. And now also that you can relate the data to the process model, we can also go into our extension of the process model. And that's something we will discuss in the next lectures. Hope to see you again soon.

Conformance checking in ProM

In this step we explain how you can apply conformance checking in ProM.

In this video we use the ‘Artificial - Loan Process.xes.gz’ event log first, and then the ‘BPI_Challenge_2012_AO.xes.gz’ event log.

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This video is from the free online course:

Introduction to Process Mining with ProM

Eindhoven University of Technology

Get a taste of this course

Find out what this course is like by previewing some of the course steps before you join:

  • Introduction
    Introduction
    video

    Introduction to process mining: recognizing event data, what is process mining and what can process mining analyse.

  • Installing ProM lite
    Installing ProM lite
    video

    In this step we show how to find and install the free and open source process mining tool ProM lite.

  • Using ProM lite
    Using ProM lite
    video

    In this lecture we show the basic concepts and usage of ProM (lite): the resource, action and visualization perspectives.

  • Event logs
    Event logs
    video

    In this lecture we explain what an event log is and how it is structured. We also explain the most common attributes found in an XES event log.

  • Event logs in ProM
    Event logs in ProM
    video

    In this lecture we show you how you can load an event log in ProM and how you can get initial insights in the contents.

  • Converting a CSV file to an event log
    Converting a CSV file to an event log
    video

    Most data is not recorded in event log format. In this video we explain how a CSV file can be converted to an event log.

  • Exploring event logs with the dotted chart
    Exploring event logs with the dotted chart
    video

    After loading an event log into ProM it is important to apply the dotted chart to get initial process insights before process models are discovered.

  • Filtering event logs
    Filtering event logs
    video

    Before good quality process models can be discovered the event log data needs to be filtered to contain only completed cases for instance.

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