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Master Projects


Possible assignments

Business Process Mining and Modeling at Amsterdam municipality (4 positions)

Amsterdam is a dynamic metropolis with great ambitions: Creating an excellent urban climate of living, working and leisure, but also a decisive and effective government. The city is a 'living lab', where metropolitan tasks are both a challenge and an opportunity to develop and apply new insights, technologies and practices.

In this context, Amsterdam municipality is looking for 4 thesis interns (WO) Business Process Mining and Modeling who are able to gain insight into existing process equipment and systems in the municipal information chains. Nederlands versie, English version

Your tasks

Amsterdam municipality is looking for 4 thesis interns (WO) Business Process Mining and Modeling who are able to gain insight into existing process equipment and systems in the municipal information chains within the domains Subsidies, Finance, Public Space and -Licenses. On the basis of your research, you will make proposals for improving the process and system design, where you also look specifically at the relationship and links with the use and use of high-quality data and data services (APIs).

Your research offers starting points for the calculation and substantiation of the quantitative and qualitative yield of high-quality information in the processes. To this end, you work together in a multi-level network with members of the Innovation Team CTO, Basisinformatie, Datalab, Resources and Control and IV Organization.

In consultation with you, the Analytics for Information Systems (AIS) department at the TU / e and the municipality of Amsterdam, the thesis assignment, research hypotheses and research questions will be further elaborated. You will have time and space to work on your thesis on the job.

Your profile

- You are in the master phase of a university study (such as Business & ICT, Information Science, Mechanical Engineering, Physics, Mathematics, Informatics, Data Science, Industrial Engineering or Econometrics; - You have strong analytical skills; - You have knowledge and experience with methods and techniques such as modeling business processes, workflow management, process mining and tooling for Business Intelligence (BI) research. - You are communicative and able to come up with good solutions in a team;

Workplace and fees

Internship and travel allowances are available (in accordance with internship conditions Amsterdam). The trainee is obliged to submit a Declaration on Behavior (VOG). You will receive a physical workplace at one of the organizational units of the Innovation team CTO, Basic information, Services and / or IV organizations within the domains. Hours and division of labor in consultation.

Interested or Questions?

If you are interested or if you have questions about the vacancy, please contact Paul Geerts, Municipality of Amsterdam / 020-2539152 / 0613515737

Using Process Mining to find lead indicators of Quality Issues

Using process mining in combination with (more regular) data analysis to predict the chance of part failure during EUV assembly and testing.


ASML is a worldwide market leader in the production of lithography systems. These systems are used by customers like Intel and Samsung for their production of integrated circuits. Lithography systems are large (container sized), expensive (between 15 and 100 million dollar) and high-tech (over 50% of 16k FTE work at Development & Engineering). Introduction of new system types has been a key competitive driver of ASMLs growth over the last 30 years. The EUV Factory produces ASMLs latest system types. You can find more information about ASML here and here.


The EUV factory experiences part related quality issues during the assembly and qualification of our systems. There are projects ongoing to reduce these quality issues by finding them as early as possible in the supply chain. Reasons for these quality issues (broken parts) are diverse. They range from handling problems in warehouses to wrong packaging defined in the part administration. Another potential problem is the usage of refurbished (repaired, formerly broken) parts. What all these problems have in common is that they might be found beforehand by combining part characteristics as captured in regular part documentation (SAP database) and knowledge about the history of where the part has been before in enters the factory. We believe we can use process mining in combination with (statistical) analyses to find some of these quality issues before they cause problems in the factory. It would be interesting for us to know to which extend this is possible and how we could go about doing so in a more structural way in the future.

Detecting root causes of complaints and investigating the continuation within the customer journey

In the Dutch health care system health care insurance is obligated for all residents. The government sets the basis package and insurers compete based on price and service. Customer service is therefore very important for every health insurance company; especially in the fast changing digital world. As a result customer satisfaction is the most important KPI. Complaints are related to customer satisfaction, because they have a highly negative effect on customer satisfaction. Therefore, it is crucial to prevent customers from having any complaints. Customers do not submit a complaint out of the blue: a complaint is a reaction to one or more occurrences in the past. The best way to understand the triggers, or root causes, it to understand the touchpoints prior to a complaint.

CZ aims for a method to prevent complaints (in order to increase customer satisfaction and lower churn). When taking the touchpoints of the customer journey into account, complaint root causes can be detected and CZ can prevent future complaints. Moreover, the touchpoints after the complaint give insights about an optimal follow up after the complaint.

Prior research development

Underlined works together with several companies like CZ, Aegon and SNS to build a generic framework in which all customer contacts are brought together as a unique dataset which is further enriched by Underlined with relevant analyses that can be linked to customer events. Underlined co-created customer journey mining algorithms, together with the TU/e (Bart Hompes and Joos Buijs from the Architecture of Information Systems group). This research showed that it is possible to distinguish the different journeys per customer. Moreover, in collaboration with the TiU econometrist department, a driver model has recently developed to distinguish relevant drivers and to predict the NPS-score.

Research goal and questions:

In further collaborative research between TU/e, CZ and Underlined, the research focus will be on developing a predictive method on complaints, based on process mining techniques. The scope of the current master project will be developing a complaint driver model by taking into account the customer journey to detect root causes and follow up triggers. More precisely, after a proper preprocessing of the different types of data, the master project will start by addressing the following list of questions:

  1. Can customers be segmented? Which distance measure is suitable for clustering customers such that similar customers can be grouped based on their behavior throughout their journey? How good is this clustering method when including the domain knowledge and/or internal quality evaluation measures?
  2. What has happened ? What is the model of the current complaining process ? How does this differs among the different customers? Which sources of data are relevant to enrich this model?
  3. Why did it happen? How does data from the different cases of each segment decides different paths within the model? Is this in line with the expectations of the business owner?
  4. What will happen? Based on the above segmentation, can we predict the category of the current customer, using their characteristics and behavior ?

In the above-mentioned analysis, all available data will be leveraged. This includes logged data of the journey (workflow process data, call center data, online click trails, social media data), online and offline feedback and non-transactional data (product and background information).

About Underlined

Underlined has a proven approach and toolset for providing insights how to improve customer experience during the customer journey. Using data from customer contact channels, online environment, customer feedback and research response, we can reveal the actual customer journey and customer experience. This enables companies to measure the customer journey of their customers’ behaviour and emotions continuously and to manage and improve the journey across all channels. Underlined calls this Customer Journey Management. More information about Underlined can be found at our website:

About CZ

CZ is one of the largest health insurance companies in the Netherlands. The core activity of CZ group is the implementation of compulsory insurance against medical expenses: the basic insurance. In addition, we also offer supplementary insurance for health risks that are not covered by the basic insurance. In addition, we offer specific products for employers, such as group health insurance and occupational health programs. More information about CZ can be found at our website:


For more information, contact Marwan Hassani.

Advanced Process Mining techniques in Practice (several Master projects with ProcessGold)

ProcessGold is a software supplier bringing together Process Mining and Business Intelligence, driven by highly skilled ICT entrepreneurs and backed by a wealth of experience. ProcessGold recently released a new Process Mining platform, the ProcessGold Enterprise Platform, that combines data extraction, process mining techniques, and visual analytics in order to produce dynamic visual reports which are easy to monitor and analyze for process stakeholders. These reports form the basis for deeper, fact-driven analysis and continuous process improvement projects.

In this context, ProcessGold is constantly offering graduation internships for investigating new techniques and methodologies in Process Mining and their application in a business context. A few example topics are given below - the specific graduation project and scope and will be further developed in mutual agreement.

Possible Graduation Project Topics

  • Enhancing the Inductive Miner. The inductive miner is a process mining algorithm that can inductively discover structures, such as choice or parallelism, from event logs. We would like to investigate how we can integrate the inductive miner into ProcessGold, and where, if possible, we can improve on the algorithm to suit our practical needs.
  • Conformance checking using BPMN. Business Process Model and Notation (BPMN) can be used to model desired or expected process behavior. We would like to investigate how we can import BPMN models into ProcessGold and how to integrate conformance checking into our platform using BPMN models.
  • Prediction/simulation of throughput times. We would like to investigate how we can predict the throughput times in a process based on its mined model using predictors or simulation.
  • Process mining with user access rights. In some organizations, not all analysts may be allowed to see all details of their organization’s process or in some cases even some parts of the process. We would like to investigate how we can take these access rights into account while still providing the user with a process that can lead to meaningful insights.
  • Multi-instanced processes. Many processes have hierarchical cases that split up into multiple sub-cases that merge again later. For example, the process of manufacturing a car. Typically, these sub-cases are independent of each other, which leads to parallelism in traditional process mining. We would like to investigate how to generate insights in these kinds of processes. We will look at a pathology use case where the cases consist of a hierarchy of sub-processes.
  • Social analysis. Social interaction is an important factor within processes where people are working together. A common approach to get insights is a social network. We would like to investigate other approaches to analyze the social interactions that happen within a process.
  • Process flows on maps. Some processes can be expressed as a flow, where goods, such as packages, cargo, or money, or physical objects, such as cars or vessels, flow between predefined geographic locations. We would like to investigate how we can interactively visualize these flows on a map to enable our users to explore, understand, and find anomalies in them.
  • Interactive grouping of processes. Process data often consists of multiple sub-processes or groups of cases that exhibit similar behavior. Displaying and analyzing all these cases as a single process model may be difficult and confusing. Therefore, we would like to investigate how we can let the user interactively separate the cases of these processes into meaningful groups that can be explored separately.
  • Distributed calculation of expressions. The ProcessGold platform owes much of its flexibility to an internal expression language. This expression language is used to compute a wide variety of expressions on a very large number of records. Currently, these expressions are computed on a single thread and may form a bottleneck. We would like to investigate how we can distribute the calculation of these expressions over multiple threads to increase overall performance.
  • Performance. As the scale of the data of our customers grows, the shear amount of calculations that need to be performed on the data grows as well. To increase overall performance, we would like to investigate strategies such as flattening the data model, resorting data, and decreasing indirections.
  • Evaluation of moving calculations to a SQL or MapReduce backend. As the scale of the data of our customers grows, the shear amount of calculations that need to be performed on the data grows as well. As a strategy to increase performance, we would like to investigate the possibilities and impact of offloading these calculations to a SQL or MapReduce backend.

In all projects, the intern should be able work out the problem definition (in collaboration with ProcessGold and the supervisor), come up with a conceptual solution, and where applicable realize the solution in a proof-of-concept (in collaboration with ProcessGold).


For more information, please contact Dr. Dirk Fahland

Philips HUE Product Evolution Using Stream Mining of Customer Journey

Philips HUE is a connected personal lighting system. It is controlled by a range of apps and smart home devices.

To acquire Philips HUE, one starts with a starter kit that consists of a few lamps and a bridge. Subsequently, consumers decide to expand their system with additional lamps or/and physical sensors. About 50 lamps can be controlled in one system.

An example of HUE system is seen in the Figure below. It consists of a bridge, three color lamps and a dimmer switch.

Every consumer chooses a device to interact with the lights. By default, all consumers start with HUE or third-party app. There are also options in the HUE app to create routines to be able to control the lights automatically. For instance, one can create a wakeup routine so that the lights in the bedroom go on naturally in the morning. HUE can also be controlled by Philips physical sensors (motion sensor, tap, dimmer switch), voice control and other third party smart home devices. Amazon Echo, Google Home, Eneco Toon, Homekit are some of smart home devices that are linked to HUE. In addition, traditional wall switch can also be used to control the lights. For further information see Philips Lighting website.

The master thesis focusses on developing a process mining model to understand product evolution of consumers in their journey and identify the paths that contribute to a high Customer Lifetime Value (CLV). Moreover, the thesis will also explore data mining techniques that can be applied on the outcome of process mining model.

We would like to capture customer behavior in process models. This way we can understand the behavior better and systematically explore ways of influencing this. Process mining techniques can be used to extract process-related information from event logs. Process mining is the linking pin between data science and process science. Data science approaches tend to be process agonistic whereas process science approaches tend to be model-driven without considering the “evidence” hidden in the data. Process mining aims to bridge this gap. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). The interest in process mining is rising as is reflected by the growing numbers of publications, citations and commercial tools (Disco, ARIS PPM, QPR, Celonis, SNP, minit, myInvenio, Perceptive, etc.). In the academic world, ProM is the de-facto standard ( and research groups all over the world have contributed to the hundreds of ProM plug-ins available.

Understanding the evolution of customer behavior during their journey requires a temporal analysis of the upgrading events. Stream process mining of the generated models guarantees observing the evolution of the interleaving sub-processes and detecting interesting concept drifts. Stream mining is a recent subject in data mining. The task of streaming process discovery includes, among other things, working on potentially unbounded event logs. Few algorithms have been developed, that are able to perform process discovery in a single pass over the data. This master project will explore applying several streaming process mining and stream data mining techniques on the event data in order to answer the following questions:

  1. What happened? Which usage, extension and upgrade patterns do we have? How did they evolve over time? Which concept drifts exist?
  2. Why did happen? Will any, and if so which, customer segmentation help in explaining the answers to (1)? Will splitting interleaving sub-process better relate these answers to e.g. some timing aspects?
  3. What will happen? Can we predict next customer usages, extensions and/or upgrades based on our previous learned models?
  4. What is the best that could happen? How can we use our prediction knowledge in recommending certain controllable actions such that Customer Lifetime Value (CLV) (or other KPIs) can be maximized?


For more information, contact Marwan Hassani.

Process mining in Logistics - 3D Visualization and Scalable Process Mining on Big Event Data (2 Topics)

Vanderlande is the global market leader for value-added logistic process automation at airports and in the parcel market. The company is also a leading supplier of process automation solutions for warehouses. Some figures:

  • Vanderlande’s baggage handling systems move 3.7 billion pieces of luggage around the world per year.
  • Our systems are active in 600 airports including 13 of the world’s top 20.
  • More than 39 million parcels are sorted by its systems every day, which have been installed for the world’s leading parcel companies.
  • Many of the largest global e-commerce players and distribution firms have confidence in Vanderlande’s efficient and reliable solutions.

Vanderlande focuses on the optimization of its customers’ business processes and competitive positions. Through close cooperation, we strive for the improvement of our customers’ operational activities and the expansion of their logistical achievements.

For Vanderlande, it is critical that we have state-of-the-art techniques to analyze and optimize our customers’ logistics processes. Reasons are (a) the constant increasing size and complexity of our material handling solutions, (b) growing complexity of our software solutions, covering larger parts of our customers’ business processes, and © the demand for more advanced service offerings, covering logistics and business services. We believe that process mining is of high value for Vanderlande. Therefore, we work with the Eindhoven University of Technology on making process mining fit for analyzing logistics processes. In this context, we offer graduation projects on process mining and the application in our business.

Topic 1: 3D Visualisation of logistic processes

Within Vanderlande, we use a 3D library to create realistic simulation and emulation models. The topic of this graduation topic is to use this existing library within our process mining tooling; we want to visualize the logistic processes in 3D within our process mining tooling.


  • Become acquainted with both our 3D library and our process mining tooling
  • Come up with a conceptual solution to combine both
  • Realize the solution in a proof-of-concept, where the existing 3D library is connected to our process mining tooling
  • Application of the proof-of-concept on at least one system to test and validate

Topic 2: Implementation of scalable process discovery and conformance checking algorithms

Our systems generate big amounts of data. This is often a limiting factor; existing process mining tooling cannot work with multiple days of systems data. Because of this, we perform our analyses on a relative limited time-period, which reduces the certainty of the outcome of our analyses. For this, we are looking for algorithms that allow for scalable process discovery and conformance checking. This is a topic that we would like to work on with a graduate student.

Deliverables of the graduation study:

  • Problem analysis
  • Conceptual solution of algorithms that allow for scalable process discovery and conformance checking
  • Realize the solution in a proof-of-concept
  • Apply the proof-of-concept on at least one system to test and validate

More Information

For more information, please contact Dr. Dirk Fahland

Example completed master projects

Irina-Maria Ailenei

Process mining tools: A comparative analysis

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Erik Nooijen

Artifact-Centric Process Analysis, Process discovery in ERP systems

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Dennis Schunselaar

Configurable Declare

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Cosmina Cristina Niculae

Guided configuration of industry reference models

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Borana Luka

Model merging in the context of configurable process models

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Bram in 't Groen

VDSEIR - A graphical layer on top of the Octopus toolset

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If you have a master project item for this page, which may include possible master project assignments, on-going master projects, and completed master projects, please send it to Eric Verbeek.