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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.

18,554 enrolled on this course

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 seconds Hi, 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 seconds These 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 35 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 seconds And 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 seconds So 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 50 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.

Syllabus

  • Week 1

    Introduction into process mining and ProM

    • Introduction

      In this activity we will explain what process mining is and how to install the process mining tool ProM lite which we use throughout this course.

    • ProM lite

      In this activity we install and run ProM lite for the first time.

    • Event logs

      In this activity we explain what event logs are and where you can find them. We also show how to inspect an event log in ProM lite, and show you how to convert tabular data from a CSV file to the event log format.

    • Converting data into an event log

      Event logs are usually not ready-made, so some conversion is required. In this activity we show how a CSV file can be converted to an event log.

    • Exploring and filtering event logs

      Once you have an event log you need to explore it, and filter certain events and cases out.

    • Week 1 closing

      It's already time to wrap-up week 1.

  • Week 2

    Process models and process discovery

    • Process models

      In this activity you will learn how to read a process model, and how to detect if a process model is error-free (what we call 'sound').

    • Process discovery and process model quality

      How can we discover a process model from raw event data? And what are the main challenges? Also, how do we know how good a discovered process model is? In this activity we discuss both.

    • The Alpha miner

      How does the very first process discovery algorithm, the Alpha miner, work? In this activity you'll learn the basics of how the algorithm works, and how to apply this tool on event logs.

    • The Heuristics miner

      The Heuristics miner is an improvement over the Alpha miner. In this activity you'll learn how the Heuristics miner handles noise for instance, but also what the drawbacks of this algorithm are.

    • Inductive miner

      How does the Inductive miner create sound process models from event data? You'll learn in this activity.

    • Fuzzy miner

      Does a process discovery algorithm always need to produce Petri net? No! The Fuzzy miner discovers Fuzzy models. In this activity you'll learn what these are, and how these can be used to gain further insights into the event data.

    • Week 2 closing

      Let's wrap up week 2: process models and process model discovery.

  • Week 3

    Advanced process mining techniques

    • Conformance checking

      In this activity we discuss how the conformance of the event log and the process model can be evaluated.

    • Performance analysis

      In this activity we discuss how performance analysis of a process can be performed in ProM.

    • Social network analysis

      In this activity we briefly touch on the aspect of social network analysis from event logs.

    • Process mining case studies

      In this activity we discuss a concrete process mining application and provide pointers to other process mining case studies.

    • Overview of process mining activities and process types

      In this activity we give an overview of several process mining activities that can be performed and on which type of processes they can be performed.

    • Week 3 closing

      It is time to wrap up week 3 in which we discussed conformance checking and extensions, and provided

  • Week 4

    Applying what you’ve learned

    • Event log exploration and filtering

      In this activity we will collaboratively explore and filter a real event log.

    • Process discovery and conformance checking

      Now that we know the event log and have applied a filter, we now ask you to discover a process model and evaluate its quality.

    • Further process analysis

      Now that we have discovered one or more process models, we can use this to do further analysis, such as performance analysis, social network analysis and of course applying other tools.

    • Week 4 & course closing

      Closing discussion and article for the course.

When would you like to start?

Start straight away and learn at your own pace. If the course hasn’t started yet you’ll see the future date listed below.

  • Available now

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!"

Who will you learn with?

Eric is a scientific programmer at the Mathematics and Computer Science department. He is the custodian of the process mining framework ProM.

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

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.

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