Contact FutureLearn for Support
Skip main navigation
We use cookies to give you a better experience, if that’s ok you can close this message and carry on browsing. For more info read our cookies policy.
We use cookies to give you a better experience. Carry on browsing if you're happy with this, or read our cookies policy for more information.

Skip to 0 minutes and 10 secondsHi, welcome back. In this lecture, I will show you how the alpha miner can be executed in ProM and what the results are in several event logs. So we're still in the process discovery bridge between the observed data and discovering a process model but now with the alpha miner in ProM. So in the previous lecture, I've shown you that this is the Petri net that we expect from the alpha miner, given that it discovers this footprint matrix given the input data. And I've also shown you that this process model is not sound and that it has perfect replay fitness but could be improved a little bit on precision generalization and simplicity.

Skip to 0 minutes and 51 secondsSo let's see, if we give this data to the alpha miner in ProM, whether it results in the same Petri net. So let's import the loan example with the five traces that we've seen before.

Skip to 1 minute and 6 secondsSo let's press the Play button and select the alpha miner, which is actually on the top of the list, and let's start. So you have two options. One is which alpha miner version you want to use. But let's stick with the core alpha miner for now. And if you press Finish, you get exactly the process model as we've seen before in the lecture. So there are three parallel branches, but the accept and reject activities only consume two out of the three tokens, hence leaving one behind, resulting in a model that's not sound. So let's give the alpha miner the full, loan process event log. Let's open it. We select it and we again run the alpha miner.

Skip to 1 minute and 53 secondsNow you see a very similar process, but the accept and reject transitions actually have three incoming arrows. Hence, they consume all of the three tokens and this process model is actually sound. But let's see how the process-- but let's see how the alpha miner does on a real event log. So let's import the BPI challenge 2012 event log. And let's run the alpha miner on this event log. So what you see is spaghetti. So this is what we mean with spaghetti. You see a lot of arcs crossing and you cannot really understand what the process is.

Skip to 2 minutes and 39 secondsFurthermore, if you zoom in-- for instance, here, you can zoom in-- and you look at these activities, on the bottom on the left, you have some activities that are not connected. Since they are not depending on input tokens, they can fire whenever they want. Hence, these activities are always enabled, resulting in an imprecise process model. Also here for instance you have an activity that has many inputs. And here, you have many, many crossing arcs. So although I didn't check, I can safely assume that this process model is not sound. It's certainly imprecise and probably also not able to replay all the observed behavior, mainly because of many issues with the simplistic nature of the alpha miner.

Skip to 3 minutes and 29 secondsSo let's clear our workspace and draw some conclusions. So we just applied the alpha miner in ProM and several event logs. And we can conclude that the alpha miner has some issues. So the first issue is, as we've already seen in the previous lecture, that it does not guarantee soundness. So it can produce process models that are not sound. Secondly, it includes all the behavior. It does not filter out any noise. So everything that is observed is included in the Petri net. And therefore, it's usually not applicable to real life event logs because real life event logs always have particular traces that can be considered as noise. Thirdly, it has issues dealing with loops.

Skip to 4 minutes and 13 secondsSo if a particular activity repeats itself, BBB, or if you have two activities that repeated itself, B and a C, again BCB, the alpha miner has issues detecting this. As a fourth issue, it cannot discover long term dependencies. So if a choice early on in the process somehow influences choices made later on in the process, the alpha miner cannot really detect this. Therefore, all other algorithms have been created that are able to discover the process model using event logs that build on the ideas of the alpha miner and we will look at some in coming lectures. So I hope to see you again soon where we discover all the process discovery algorithms.

Alpha miner in ProM

In this video we show how the Alpha miner can be applied in ProM.

We use the ‘Artificial - Loan Process - Partial.xes.gz’ event log in this video. We also show the main shortcomings of the Alpha miner.

Share this video:

This video is from the free online course:

Introduction to Process Mining with ProM

Eindhoven University of Technology

Course highlights Get a taste of this course before you join:

  • Introduction

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

  • Installing ProM lite
    Installing ProM lite

    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

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

  • Event logs
    Event logs

    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

    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

    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

    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

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