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Master Projects

Documents

Possible assignments

Data Science: Developing a Self-Standing Dynamic reporting tool

A huge amount of (transaction) data is generated on a daily basis in ASML Development and Engineering department. The data is scattered in different sources. The challenge would be extracting data from relevant sources and creating a self-standing dynamic reporting tool (dashboard) demonstrating performance of (Supplier Quality) Engineers in different granularity levels (Department, Section, Individual) based on a set of pre-defined KPIs.

Are you a master student in Software Engineering (Data Science) with a passion on real-life data challenges? Then we are looking for you!

Job Description

You will be working closely with a team of Supplier Quality Engineers. You will get to know the mission of Supplier quality engineers and way of working in relationship with different stakeholders. You will extract relevant data from different data sources and integrate them to different levels of granularities to examine meeting certain KPIs.

You explore different ways of visualization in term of a dynamic tool (dashboard) with all relevant properties such as flexibility, scalability, etc. You have also the freedom to explore the data and create meaning out of it beyond the boundaries of a dashboard creation or this set of KPIs as long as it is self-standing. You are also free to take lead in defining KPIs not only in terms of lag but also lead KPIs (Balanced Score Card approach).

Education

Are you a master student in Software Engineering (Data Science) with a passion on real-life data challenges? Then we are looking for you!

You like to explore, learn and build cool stuff. You love working with data and that extracting relevant and easy to understand information from it. You have programing skills, Web information retrieval, Data-mining, Data Engineering, algorithms, visualization, statistics for big data, You are motivated for a challenge and are self-assured to drive a project.

You are a conceptual thinker. You are fluent in English and have good communication (reporting) skills. Your grades are excellent and you have a strong motivation for ASML as your future employer. As this is an open assignment, please attach a motivational letter with a proposal and a recent grade list to your application.

This is an apprentice internship for 5 days a week with duration of 3 to 5 months. The start date is as soon as possible.

Please keep in mind that we can only consider students (who are enrolled at a school during the whole internship period) for our internships and graduation assignments.

Other Information

What ASML offers

Your internship will be in one of the leading Dutch corporations, gaining valuable experience in a highly dynamic environment. You will receive a monthly internship allowance of 500 euro (maximum), plus a possible housing or travel allowance. In addition, you’ll get expert, practical guidance and the chance to work in and experience a dynamic, innovative team environment.

ASML: Be part of progress

We make machines that make chips – the hearts of the devices that keep us informed, entertained and safe; that improve our quality of life and help to tackle the world’s toughest problems.

We build some of the most amazing machines that you will ever see, and the software to run them. Never satisfied, we measure our performance in units that begin with pico or nano.

We believe we can always do better. We believe the winning idea can from anyone. We love what they do – not because it’s easy, but because it’s hard.

ASML: Be part of the progress

ASML is leading in the worldwide development, production and sales of high-end lithography systems for the semiconductor industry. Almost 17,000 people worldwide work at ASML at offices in the United States, Asia and at the corporate headquarters in Veldhoven. ASML employees share a passion for technology with a customer focus. At ASML, we work collectively to further develop and implement complex and high-quality technological systems. Working at ASML is therefore challenging and dynamic, with ambitious objectives and high standards key to our continuing success. But hard work here pays off: ASML invests in the development of its people and successes are shared. ASML promises mutual commitment to our growth and yours.

Join ASML’s expanding multidisciplinary teams and help us to continue pushing the boundaries of what’s possible. How will you be part of progress?

Contact

For more information, contact Renata Medeiros de Carvalho.

Real-Time Model Discovery of the Service Order Process Using Stream Process Mining

Kropman Installatietechniek is a Dutch company established in 1934 and has become one of the leading companies of the Dutch installation industry. With about 800 employees, 12 regional locations and an annual turnover of more than 100 million Euro, Kropman is an integral service provider with a multidisciplinary approach. Kropman is mainly active in office buildings, health care and industry. It offers design, construction and maintenance in the field of facility installations. Kropman also has a separate business for process installations and cleanrooms: Kropman Contamination Control. The maintenance (services) is a fast growing business line. The order process is fully supported within an ERP environment. The service order process is not a trouble-free process: the process takes too long and flows more often than necessary. The company aims at increasing the throughput of the SO process and decreasing the amount of process deviations by applying process mining and data mining techniques.

To have a better overview of the process, Kropman is aiming at:

  • Exposing bottlenecks happening in the SO process
  • Detecting deviations from the supposed model in the real time they are happening using stream process mining techniques
Contact

For more information, contact Marwan Hassani

Understanding Customer Journeys with Process Mining

In today’s customer environments, where customers use many different contact channels to solve outstanding questions and do requests, it becomes increasingly difficult to follow customer behavior, optimize service levels and provide a memorable experience for customers. Especially when the contacts of the customer are not related. These journeys can be product specific (e.g. changing your telephone provider), customer specific (e.g. change known home address) or life event specific (e.g. starting a family).

Underlined works together with several companies like CZ, Aegon and SNS to build a generic framework in which all traceable, incoming and outgoing customer contacts (call, web, e-mail, chat, etc.) are brought together as a unique dataset. The dataset of companies is further enriched by Underlined with relevant analysis that can be linked to (unique) customer events.

Underlined has worked together with the TU/e (Bart Hompes and Joos Buijs from the Architecture of Information Systems group) and CZ (one of the largest Dutch health insurance companies) to develop customer journey mining algorithms. This research showed that it is possible to distinguish the different journeys per customer without any prior process knowledge, but additional research is needed to:

  1. Apply machine learning techniques to cluster customers with similar behavior. The customer journey can significantly vary depending on the customer characteristics. Therefore, try to build a single model for all customers will lead to not fully satisfactory results. Therefore, the application of machine learning or OLAP techniques (a.k.a. as process cubes) would be beneficial to split the customers into clusters each containing customers with similar characteristics.
  2. Research multi-dimensional similarity matrix. Current customer journey mining algorithms need to know which activities might be related to each other. Information on this is stored in a similarity matrix. Currently there is a working version for relatively simple datasets and processes. We would like to develop a next level similarity matrix for more complex data which contain multiple journeys and multiple customer segments.
  3. Predictive modelling of emotions in the journey. Customers make decisions in the customer journey based on their emotions. In current datasets there is sample feedback, which expresses the feedback of customers and their concerns regarding the service of a company. We would to research a predictive model that, out the basis of the past customer journeys, it can predict the intermediate and final emotions of the customers who are currently active in their journeys.

In the above-mentioned analysis, the student should try to leverage on every piece of available data. This includes logged data of the past customer behavior, for example call center data, online click trails, social media data and online and offline feedback. But also non-transactional data like product usage and customer segmentation variables.

Masters Thesis Research

Research goal

Investigate what methods can be developed to best analyze and predict customer journeys, based on process mining techniques and principles. Understand which cluster methods could be used to identify similar customer journeys independent of the number and sequence of the journey steps. Find out what the next phase of the similarity matrix would contain to improve the quality of the obtained customer journeys and understand what drives emotions in the customer journey.

Research general scope

Develop a research method which makes it possible to analyze and understand different kinds of customer behavior regarding customer journeys. For the master thesis the scope will be limited to one specific topic or research area. This we will discuss with our customers if they are willing to open their data sources for our research.

Research Questions

The master thesis should provide insights into questions, such as:

  • Can we develop a machine learning or OLAP-based method to bundle journeys and predict what is the likely journey a customer will take based on the data?
  • What similarity metric should be used to understand the similarity between two customer journeys?
  • Can we derive what drives emotions in the customer journey?
  • Can we derive what drives behavior in the customer journey?
  • Can these insights be used to provide recommendations to improve the emotional experience?

About Underlined

Underlined has a proven approach and toolset for improving 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.

In the thesis assignment, the student will closely work with the Underlined team that cooperates with large corporate organisations, such as CZ, VGZ, Aegon, T-Mobile/BEN and SNS Bank.

More information about Underlined can be found at the company’s website: http://underlined.nl/en/

Contact

For more information, contact Massimiliano de Leoni.

Improving the Evolutionary Tree Miner

The Evolutionary Tree Miner (ETM) is a genetic process discovery algorithm that works on process trees, a specific process modelling formalism. Recently work has started on an interactive process discovery algorithm where the user is guided to modify a free-choice Petri net in such a way that the Petri net is always sound.

The master project would consist initially of ‘connecting’ the ETM to this interactive process discovery approach, hence replacing the human. The main benefit would be that the ETM does not solely work on process trees any more (which can be somewhat restrictive), but directly on Petri nets in such a way that they are guaranteed to remain sound.

After this initial step several other improvement steps can be applied to the ETM such as extending the work in deriving alignments, estimating quality of a process model, smarter mutations, smarter application of mutations, starting from solutions created by other algorithms, etc. etc.

Therefore an important part is the implementation of these ideas in the ETM which is implemented in our toolset ProM. Good programming skills in Java are therefore important, no matter if you are a BIS or a CSE student.

Contact

For more information, contact Joos Buijs.

SAP Process Mining at Ciber

Ciber Netherlands [1] is an IT consulting company, with its origins in Detroit, integrated now within the Manpower group. They have a strong interest in using Process Mining to improve their systems and the services they provide to their customers. In particular, they wish to apply Process Mining to the systems they use for managing their internal processes. Their interest is on focusing their efforts on the SAP platform, which many of their clients use as well. Performing this project on their own SAP systems would be a great way to demonstrate the potential of Process Mining in these kind of environments, which they would be able to extend for their clients. Until now, Process Mining on SAP systems has been performed in an ad-hoc fashion. However, we aim at automating this procedure and applying new techniques [2,3] to extract the relevant information. These techniques allow to retrieve historical and execution information, enabling the application of analysis methods in a more standardized and meaningful way. To apply these techniques in real-life scenarios, many challenges remain open. One of them is to be able to identify interesting views on the data in order to obtain useful representations of the process. Often, this requires the involvement of domain experts, but a more automatic way would be desirable. Therefore, the project would not only involve the application of existing techniques, but the development of new methods to identify meaningful views on the real data.

References

[1] http://ciber.nl
[2] E. González López de Murillas, Hajo A. Reijers, Wil M.P. van der Aalst, “Everything You Always Wanted to Know About Your Process, But Did Not Know How To Ask”, 2016 - PQ 2016, 1st International Workshop on Process Querying, Rio de Janeiro, Brasil. https://www.win.tue.nl/~egonzale/wp-content/uploads/2016/08/dapoqlang-eduardo-gonzalez-pq-2016.pdf
[3] E. González López de Murillas, Hajo A. Reijers, Wil M.P. van der Aalst, “Connecting Databases with Process Mining: A Meta Model and Toolset”, 2016 - BPMDS 2016, 17th International Conference, Ljubljana, Slovenia. https://www.win.tue.nl/~egonzale/wp-content/uploads/2016/05/connecting-databases-eduardogonzalez-bpmds-2016.pdf

Contact

For more information, please contact Eduardo González Lopéz de Murillas.

Example completed master projects

Bram in 't Groen

VDSEIR - A graphical layer on top of the Octopus toolset

Description

In his work, Bram in 't Groen introduces a graphical representation for DSEIR (a language used in the Octopus toolset for designing embedded systems) called Visual DSEIR (VDSEIR). By using VDSEIR, users of the toolset can create specifications in DSEIR by means of creating graphical models, removing the need for those users to know how to program in the Octopus API. Bram in 't Groen provides a model transformation from VDSEIR to DSEIR that makes use of an intermediate generator model and a parser that is automatically generated from an annotated JavaCC grammar. The graphical representation for DSEIR consists of several perspectives and it contains a special form of syntactic sugar, namely hierarchy. It is possible to create hierarchical models in the graphical representation without having support for hierarchy in the original DSEIR language, because these hierarchical models can be translated into non-hierarchical DSEIR models. This way, additional expressiveness is created for the user, without modifying the underlying toolset.

Type

AIS / External / ESI

Borana Luka

Model merging in the context of configurable process models

Description

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.

Type

AIS / Internal / CoSeLog project involving 10 municpalities

Links

Staff involved

Cosmina Cristina Niculae

Guided configuration of industry reference models

Description

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.

Type

AIS / External / To-Increase

Links

Staff involved

Dennis Schunselaar

Configurable Declare

Description

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.

Type

AIS / Internal

Links

Staff involved

Erik Nooijen

Artifact-Centric Process Analysis, Process discovery in ERP systems

Description

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.

Type

AIS / External / Sligro

Links

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.