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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.

Example completed master projects

Irina-Maria Ailenei

Process mining tools: A comparative analysis


In her thesis, Irina-Maria Ailenei proposes an evaluation framework that is used to assess the strengths and the weaknesses of process mining tools. She applied the framework in practice for evaluating four process mining systems: ARIS PPM, Flow, Futura Reflect, and ProcessAnalyzer. The framework is based on a collection of use cases. A use case consists of a typical application of process mining functionality in a practical situation. The set of use cases was collected based on the functionality available in ProM and was validated by conducting a series of semi-structured interviews with process mining users and by conducting a survey. The validated collection of use cases formed the base of her tool evaluation. The project was conducted within Fluxicon and was created based on a request from Siemens. The work also resulted in a paper presented at the BPI workshop in Clermont-Ferrand in 2011.


AIS / External / Fluxicon

Staff involved

Erik Nooijen

Artifact-Centric Process Analysis, Process discovery in ERP systems


In his thesis, Erik Nooijen developed an automated technique for discovering process models from enterprise resource planning (ERP) systems. In such systems, several different processes interact together to maintain the resources of a business, where all information about the business resources are stored in a very large relational database. The challenge for discovering processes from ERP system data, is to identify from arbitrary tables how many different processes exist in the system, to extract event data for each instance of each process in the system. Erik Nooijen identified a number of data mining techniques that can solve these challenges and integrated them in a software tool, so that he can automatically extract for a process of the ERP system an event log containing all events of that process. Then classical process discovery techniques allow to show the different process models of the system. Erik conducted his project within Sligro where he is actively using his software to improve the company's processes. The thesis resulted in a workshop paper presented at the International Conference on Business Process Management 2012 in Tallinn, Estonia.


AIS / External / Sligro


Staff involved

Dennis Schunselaar

Configurable Declare


Declarative languages are becoming more popular for modeling business processes with a high degree of variability. Unlike procedural languages, where the models define what is to be done, a declarative model specifies what behavior is not allowed, using constraints on process events. In his thesis, Dennis Schunselaar studies how to support configurability in such a declarative setting. He takes Declare as an example of a declarative process modeling language and introduces Configurable Declare. Configurability is achieved by using configuration options for event hiding and constraint omission. He illustrated our approach using a case study, based on process models of ten Dutch municipalities. A Configurable Declare model is constructed supporting the variations within these municipalities.


AIS / Internal


Staff involved

Cosmina Cristina Niculae

Guided configuration of industry reference models


Configurable process models are compact representations of process families, capturing both the similarities and differences of business processes and further allowing for the individualization of such processes in line with particular requirements. Such a representation of business processes can be adopted in the consultancy sector and especially in the ERP market, as ERP systems represent general solutions applicable for a range of industries and need further configuration before being implemented to particular organizations. Configurable process models can potentially bring several benefits when used in practice, such as faster delivery times in project implementations or standardization of business processes. Cosmina Niculae conducted her project within To-Increase B.V., a company that specializes in ERP implementations. She developed an approach to make configuration much easier, implemented it, and tested it on real-life cases within To-Increase.


AIS / External / To-Increase


Staff involved

Borana Luka

Model merging in the context of configurable process models


While the role of business process models in the operation of modern organizations becomes more and more prominent, configurable process models have recently emerged as an approach that can facilitate their reuse, thereby helping to reduce costs and effort. Configurable models incorporate the behavior of several model variants into one model, which can be configured and individualized as necessary. The creation of configurable models is a complicated process, and tool support for it is in its early steps. In her thesis, Borana Luka evaluates two existing approaches to process model merging which are supported by tools and test an approach to model merging based on the similarity between models. Borana’s work resulted in a paper presented in the 2011 International Workshop on Process Model Collections.


AIS / Internal / CoSeLog project involving 10 municpalities


Staff involved

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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.