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Online course

Introduction to Process Mining with ProM

Learn how to use the free, open source process mining framework (ProM) to analyse, visualise, and improve processes based on data.

What’s the difference between a free course and an upgraded course?

Free:

  • Access to the course for its duration + 14 days, regardless of when you join (this includes access to articles, videos, peer review steps, quizzes)
  • No access to course tests
  • No certificate

Upgraded:

  • Unlimited access to the course, for as long as it exists on FutureLearn (this includes access to articles, videos, peer review steps, quizzes)
  • Access to course tests
  • A Certificate of Achievement when you complete the course

Find out more

Introduction to Process Mining with ProM

Learn to get a critical, process-centric perspective on data

Process mining combines business process management with data science. Using process mining, you can analyse and visualise business processes based on event data recorded in event logs. For example, you could analyse how people use public transportation; verify whether a loan application is processed correctly by a bank; or predict when hardware parts are likely to fail. This online course will give you an introduction to this new and exciting field.

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Skip to 0 minutes and 10 secondsHi, my name is Joos Buijs and I'm assistant professor at Eindhoven University of Technology. In this trailer, I would like to show you what you will learn when you follow the Future Learn MOOC, Process Mining with ProM. We start from the observation that event data is everywhere. Whenever you do a bank transaction, whenever you send an email, or receive a phone call, even when you use the public transportation system, event date is created. Similarly, when you use apps on your smartphone, even when you use your smart TV, or whenever you browse a website, even the Future Learn website event data is recorded. So what we observe is all these are software systems. They handle particular features, public transport, websites, etc.

Skip to 0 minutes and 55 secondsThese systems somehow have to interact with the real world. And by default, a software system doesn't know how. So a process model is used to configure the software system and explain how the real world will interact with the software system. So in this process model, it's described which activities this software system should do and how to interact with particular triggers from the real world. Now when the real world starts interacting with the software system, data is created. And this is what we call event logs. And event logs contain data in a particular format, mainly what happened, when, and for which case. And using solely this data, we can do process mining.

Skip to 1 minute and 38 seconds So process mining is the connection between this event data and the process model used to configure the software system. One of the key features of process mining is process discovery. Using solely the data of what happened when and for which case, we can discover a process model describing how the software system behaves in different circumstances. Additionally, when you have a process model that you use to configure the system, you can do conformance checking. Where is the data deviating from the model that you had in mind? Finally, you can also extend the process model. Where is time spent in a particular process? How are resources working together? All this can be done using process mining.

Skip to 2 minutes and 19 secondsAnd in this course, we will teach you how. And for this, we used the process mining tool ProM. ProM Lite contains 100 plus plugins, well-tested, and it's free and open source. So even in the end, you can contribute yourself new features to ProM. With ProM, you can convert tabular data into an event log format and start process mining. You get initial statistics. You get an overview of what happened when. And here, you can see several patterns. And you will explain how you can recognize particular things in for instance a dotted chart. But also, we will show you how you can fully automatically discover a process model and animate the real data on top of this process model.

Skip to 3 minutes and 2 secondsSo in this case, every yellow dot is a case. Activities have been performed for each yellow dot. And whenever you pause this animation, you know exactly what the status was at a particular point in time. And this is all done fully automatically, including the resource and performance dimensions. All discovered fully automatic. In this course, we will teach you all activities necessary to execute a process mining project, from the planning and scoping phase to the extraction phase. What data do you need to do process mining? However, we will focus on several process mining techniques and how you can evaluate the results. Depending on the results, you may need to filter the data further to get to real and good quality models.

Skip to 3 minutes and 49 seconds Then, we show you how you can summarize results into actionable insights. So from process mining analysis results, you go from real actionable insights that lead to process improvement. So at the end of this course, you are able to do a process mining project from start to end on real data from your organization. Therefore, I hope to see you soon at this course.

What topics will you cover?

Process mining provides a critical, process-centric perspective on data, which is not available with classical data mining or machine learning techniques. For example, with process mining you can:

  • analyse how people use public transportation;
  • verify whether the loan application process in a bank is executed correctly;
  • gain insights in customer journeys on a website and combine this with interactions on different channels such as phone and e-mail;
  • analyse learning behaviour of students in a MOOC to improve course contents;
  • predict when hardware parts are likely to fail.

Week 1 introduced process mining and showed that event data is everywhere. Furthermore, it explained how you can translate event data that you might have for use in our tool. We also discussed how to start investigating the event log and how to filter the event log based on these insights for further analysis.

Week 2 discussed process models and how to evaluate how good a process model is with respect to the data. We also demonstrated several techniques to automatically learn a process model from the data, and discussed the pros and cons of each approach.

Week 3 covered additional process mining techniques such as conformance checking, performance analysis and social network analysis. This week also showed process mining application examples.

Week 4 provided learners with time to work on the peer assignment where they were asked to write a real process mining report on real-life event data.

After this course learners can:

  • work with the free and open source process mining tool ProM;
  • process raw event data into an event log for further analysis;
  • execute core process mining analysis techniques, and correctly interpret the results;
  • gain concrete and actionable process insights from (their own) event data.

When would you like to start?

  • Available now
  • Date to be announced

What will you achieve?

By the end of the course, you'll be able to...

  • Identify event data suitable for analysis
  • Apply various process mining techniques in the ProM lite tool to event logs
  • Interpret the results of various process mining techniques in the ProM lite tool
  • Describe the process flow, based on the event data
  • Improve processes based on process mining analysis of the event data

Who is the course for?

This course is designed for anyone with an interest in business process management (BPM), data science and/or process analytics. No specific prior knowledge is required, only a healthy interest is required.

We do however require you to have a computer with internet access ready on which you can install and use ProM. Please refer to www.promtools.org for more information regarding ProM.

What do people say about this course?

I've really enjoyed this challenging, yet rewarding course. It's highly likely that I'll apply process mining and ProM in my work as a business software engineer/analyst. I still have so many unanswered questions (around Petri nets, alignments and sojourn times to name a few) but I intend to buy the "Godfather's" book which I hope will clear things up :D. Dank je wel voor de kursus, Professor Buijs. Dat besonders!

Daniel Rhodes

Who will you learn with?

Joos Buijs

Assistant professor at Eindhoven University of Technology at the Data Science Center (DSC/e) and research group of prof. dr. ir. Wil van der Aalst on process mining.

Who developed the course?

Eindhoven University of Technology (TU/e) is a research-driven, design-oriented university of technology with a strong international focus. The university was founded in 1956 and has 8,500 students.

Buy a personalised, digital and printed certificate and transcript

You can buy a Certificate of Achievement for this course — a personalised certificate and transcript in both digital and printed formats, to prove what you’ve learnt. A Statement of Participation is also available for this course.

Certificate of Achievement + transcript £59.00

Statement of Participation £39.00

Estimate prices in preferred currency

Charges to your account will be made in GBP. Prices in local currency are provided as a convenience and are only an estimate based on current exchange rates.

Course highlights Get a taste of this course 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.