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13 December 2023

The award goes to....  

PhD Student Axel Christfort and Supervisor Associate Professor Tijs Slaats from the University of Copenhagen won the Process Discovery Contest at the 5th International Conference on Process Mining with their DisCoveR miner.

In a remarkable achievement, PhD student Axel Christfort and his supervisor, Associate Professor Tijs Slaats, won the Process Discovery Contest at the 5th International Conference on Process Mining.

Their cutting-edge DisCoveR miner produced the most accurate models and stood as the sole algorithm to successfully complete discovery and classification tasks within the stipulated time.

Process discovery algorithms play a crucial role in analyzing event logs, generating human-readable models that elucidate the behavior captured in the log. This includes understanding how individuals sequence activities in their work processes. The ICPM Conference, organizers of the Process Discovery Contest, evaluate submissions based on accuracy, requiring participants to mine models for a diverse range of logs and correctly classify corresponding ground truth traces.

This is the third prize in the Process Discovery Contest for the Process Modelling and Intelligence group from the Department of Computer Science, University of Copenhagen. In 2021, they secured awards for the best overall and the best imperative miner. The DisCoveR miner.

DisCoveR originated from a M.Sc. thesis by Viktorija Sali and Andrew Tristan Parli, supervised by Professor Slaats. The algorithm has undergone further refinement by Industrial PhD Student Christoffer Olling Back from ServiceNow, with ongoing enhancements by Axel Christfort. Funding from Independent Research Fund Denmark, DIREC – Digital Research Centre Denmark, and Innovation Fund Denmark has been instrumental in supporting this groundbreaking work.

Axel Christfort and Tijs Slaats are nominated Process Discovery Contest Winners

The industrial application of DisCoveR has been demonstrated through its implementation by DCR Solutions. The algorithm’s efficacy and utility have been validated in real-world scenarios, emphasizing its practical significance. Ongoing contributions from PhD Vlad Paul Cosma and Professor Thomas Hildebrandt have further extended and improved the miner, adding to its robustness.

Looking ahead, the Process Modelling and Intelligence group is eager to build upon these achievements to secure additional funding and foster novel collaborations. The team is already gearing up for the next iteration of ICPM, aiming to continue their winning streak and further advance the field of process discovery.


Associate Professor Tijs Slaats is the project manager of the DIREC project ‘AI and Blockchain for complex business processes’.

Together with industry, the project aims to develop methods and tools that enable the industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, with a specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.  


Meet Tijs Slaats, who just won a prize for best process mining algorithm

Meet Tijs Slaats, who just won a prize for best process mining algorithm

Tijs is Associate Professor at the Department of Computer Science at the University of Copenhagen and Head of the Business Process Modeling and Intelligence research group. In DIREC, he works on the Bridge project AI and Blockchains for Complex Business Processes.

Tijs’ research interests include declarative and hybrid process technologies, blockchain technologies, process mining, and information systems development.  

He co-invented the flagship declarative Dynamic Condition Response (DCR) Graphs process notation and was a primary driver in its early commercial adoption. In addition, he led the invention and development of the DisCoveR process miner, which was recognized as the best process discovery algorithm in 2021. 

Can you tell us briefly about your research and what value you expect to get from it?
We try to describe processes. It can be basic things that we do as human beings. It could be assembling a car at a factory, but it could also be treating patients at a hospital. If a patient is admitted to a hospital, they need help and treatment.

What it has in common for these examples is that you need to go through a number of steps and activities to reach your goal, and those activities are related to each other. It may be medication that needs to be taken in a certain order.

In our research, we have developed a mathematical method for describing these processes. The reason for doing this is because it gives you the tools to ensure that the process goes the way you want it to.

In the new DIREC project, we take one step further. We have observed that many companies and organizations have large amounts of data on how they have performed their jobs. And we can look at these data and analyze them to see how they actually perform their jobs, because the way many people do their jobs does not necessarily match the way they expect to do it. Maybe they take shortcuts unintentionally.

Our idea is to find these data and analyze them and on that basis we get a model.

It is important that such a model also is understandable to the users so that they can understand how they perform their work. We call this process mining, and it is a reasonably large academic area. Two years ago, I developed an algorithm, and it was in a contest, where you compare which algorithm is most accurate to describe these “logs of behaviour”, and we won the contest.

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What results do you expect from your research?
Our cooperation with industry is particularly important. In the project, we collaborate with the company Gekkobrain, which works with DevOps, and they are interested in analyzing large ERP systems and in finding tools that can optimize a system and find abnormalities. These systems are quite complex, so it is important to be able to identify where things are going wrong.

Gekkobrain has a lot of data because they work with large companies that have huge amounts of log data, and these systems are so complex that it adds some extra challenges for our algorithms. To get access to such complex data is an important perspective.

How can your research make a difference to companies and society?
The biggest impact of our work and models is that you can gain insight into how you perform your work. It gives you an objective picture of what has been done.

Companies can use it to find out if there are places where work processes are performed in an inappropriate way and thus avoid the extra costs.

Can you tell us about your background and how you ended up working with this research area?
I initially got a Bachelor degree in Information & Communication Technology from Fontys University of Professional Education, then worked in industry where I led the webshop development team of a Dutch e-commerce provider and acted as project leader on the implementation of our product for two major customers; Ferrari and Hewlett Packard.

I decided to move to Denmark after meeting my (Danish) wife, at the time I was already considering pursuing further education, while my wife was fairly settled in Denmark, so it made sense for me to be the one to move.

I got my MSc and PhD degrees at the IT University of Copenhagen. There I became interested in the field of business process modeling because it allows me to combine foundational theoretical research with very concrete industrial applications. Process mining in particular provides really interesting challenges because it requires learned models to be understandable for business users, something that has only recently come into focus in the more general field of AI. 

After a short postdoc at ITU I accepted a tenure-track assistant professorship at DIKU, which was a very good opportunity because it offers a (near) permanent position for relatively junior researchers. At the time this was uncommon in Denmark.