Internship/Thesis vacancies at KPMG

The Information Systems group is often contacted by industrial organizations with assignments that could lead to student projects at various levels (master thesis project, bachelor completion project, etc.) Through this website we make these topics available to our students. In this post, we announce one such project, involving company: KPMG

Vacancy titles:

Primary Contacts

  • within TU/e: Pieter Van Gorp
  • within KPMG: Wesley van Renswouw

The organization (in Dutch)

KPMG Nederland biedt hoogwaardige dienstverlening op het gebied van audit en advisory – KPMG Meijburg & Co is specialist in tax. Zo adviseren we onder meer over prestatieverbetering en risicobeheersing, begeleiden we transacties en zijn we actief op het gebied van controle en verantwoording. En we zijn onderdeel van een groot internationaal netwerk.

KPMG - IT Advisory - In de afgelopen jaren is de IT-agenda steeds omvangrijker en complexer geworden. De aantrekkende economie zorgt ervoor dat organisaties weer nieuwe initiatieven nemen en oog hebben voor de mogelijkheden die de nieuwe technologieën bieden. In tijden van verandering is het niet alleen belangrijk om op de kosten te letten, maar tevens om de complexiteit te reduceren. Zodat je als organisatie wendbaar bent en eenvoudig in kan spelen op diverse ontwikkelingen.


Technologische ontwikkelingen gaan snel en er wordt steeds meer mogelijk. Het is zaak om vragen die in de business leven te vertalen naar relevante IT-toepassingen. Daar ondersteunt KPMG in. We maken het technische landschap van een organisatie minder complex. En helpen haar beter voorbereid te zijn op de toekomst.

Specific Areas of our KPMG contact:

Colloquium IS Claudia Chituc:

Claudia Chituc

The business value of big data: opportunities and challenges

Information and communication technologies are used to store and analyze increasing amounts of digital data.  Although numerous advantages are associated with big data, research on the business value of big data is scarce. This presentation will report on some projects undertook towards identifying opportunities for obtaining business value out of big data.  Two models will be discussed, which provide support (e.g., to decision makers/ stakeholders) in understanding how to derive value from big data.

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New Employee: Yingqian Zhang

zhang-websiteI received my Ph.D. degree in Computer Science from University of Manchester in UK. Before joining TU Eindhoven, I worked as an assistant professor in the Department of Econometrics at Erasmus University Rotterdam.  Earlier, I worked in the Algorithmics group at TU Delft, in the Computational Intelligence group at TU Clausthal (Germany), and in the Institute for Advanced Computer Studies at University of Maryland, College Park, USA.

I consider myself an “Artificial Intelligence” researcher. During my Ph.D and post-doc period, I developed algorithms and mechanisms to solve multi-agent problems, i.e., task allocation, planning and scheduling problems that involve cooperative or self-interested players. Over the past a few years, I have been also investigating how data can help to improve decision making and optimization. For example, I designed machine learning methods to learn bidders’ behaviour models, and used the learned models to optimize auction design.

At TUE, I will continue my research on data driven decision making,  algorithm design, (computational) game theory, and multi-agent systems.  In addition, I am looking forward to learning new knowledge, tackling new application domains, and establishing new cooperations!

Colloquium IS Shaya Pourmirza: Correlation Mining: Mining Process Orchestrations without Case Identifiers

Shaya Pourmirza, Remco Dijkman, Paul Grefen

Correlation Mining: Mining Process Orchestrations without Case Identifiers

Process discovery algorithms aim to capture process orchestration models from event logs. These algorithms have been designed for logs in which events that belong to the same case are related to each other – and to that case – by means of a unique case identifier. However, in service oriented systems these case identifiers are usually not stored beyond request-response pairs, which makes it hard to relate events that belong to the same case. This is known as the correlation challenge. This paper addresses the correlation challenge by introducing a new process discovery algorithm, called the correlation miner, that facilitates process discovery when events are not associated with a case identifier. Experiments performed on both synthetic and real-world event logs show the applicability of the correlation miner.

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PhD defense Jan Claes

Cover_JanMore information can be found on:

In order to download the thesis, follow:





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Lately, the focus of organizations is changing fundamentally. Where they used to spend almost exclusively attention to results, in terms of goods, services, revenue and costs, they are now concerned about the efficiency of their business processes. Each step of the business processes needs to be known, controlled and optimized. This explains the huge effort that many organizations currently put into the mapping of their processes in so-called (business) process models.

Unfortunately, sometimes these models do not (completely) reflect the business reality or the reader of the model does not interpret the represented information as intended. Hence, whereas on the one hand we observe how organizations are attaching increasing importance to these models, on the other hand we notice how the quality of process models in companies often proves to be insufficient.

The doctoral research makes a significant contribution in this context. This work investigates in detail how people create process models and why and when this goes wrong. A better understanding of current process modeling practice will form the basis for the development of concrete guidelines that result in the construction of better process models in the future.  The first study investigated how we can represent the approach of different modelers in a cognitive effective way, in order to facilitate knowledge building. For this purpose the PPMChart was developed. It represents the different operations of a modeler in a modeling tool in such a way that patterns in their way of working can be detected easily. Through the collection of 704 unique modeling executions (a joint contribution of several authors in the research domain), and through the development of a concrete implementation of the visualization, it became possible to gather a great amount of insights about how different people work in different situations while modeling a concrete process.

The second study explored, based on the discovered modeling patterns of the first study, the potential relations between how process models were being constructed and which quality was delivered. To be precise, three modeling patterns from the previous study were investigated further in their relation with the understandability of the produced process model. By comparing the PPMCharts that show these patterns with corresponding process models, a connection was found in each case. It was noticed that when a process model was constructed in consecutive blocks (i.e., in a structured way), a better understandable process model was produced. A second relation stated that modelers who (frequently) moved (many) model elements during modeling usually created a less understandable model. The third connection was found between the amount of time spent at constructing the model and a declining understandability of the resulting model. These relations were established graphically on paper, but were also confirmed by a simple statistical analysis.

The third study selected one of the relations from the previous study, i.e., the relation between structured modeling and model quality, and investigated this relation in more detail. Again, the PPMChart was used, which has lead to the identification of different ways of structured process modeling. When a task is difficult, people will spontaneously split up this task in sub-tasks that are executed consecutively (instead of simultaneously). Structuring is the way in which the splitting of tasks is handled. It was found that when this happens consistently and according to certain logic, modeling became more effective and more efficient. Effective because a process model was created with less syntactic and semantic errors and efficient because it took less time and modeling operations. Still, we noticed that splitting up the modeling in sub-tasks in a structured way, did not always lead to a positive result. This can be explained by some people structuring the modeling in the wrong way. Our brain has cognitive preferences that cause certain ways of working not to fit. The study identified three important cognitive preferences: does one have a sequential or a global learning style, how context-dependent one is and how big one”s desire and need for structure is. The Structured Process Modeling Theory was developed, which captures these relations and which can form the basis for the development of an optimal individual approach to process modeling. In our opinion the theory has the potential to also be applicable in a broader context and to help solving various types of problems effectively and efficiently.

New Employee: Jonnro Erasmus

JErasmus_I was born and raised in Pretoria, South Africa. I’ve unfortunately never had a lion or a giraffe for a pet though. In 2008 I earned a Bachelor of industrial engineering from the University of Pretoria and in 2012 I completed a Master of Engineering Management from the University of Johannesburg. I’ve spent about six years as an industrial engineer in the electricity and manufacturing industry sectors. My recent research looked into the way Product Lifecycle Management technology can be used to enable collaboration across the product value chain. I’m interested in anything related to business processes, systems engineering and complexity theory. I now aim to make use of my experience and knowledge to contribute as much as possible to the HORSE project (I still don’t know what the acronym is for).

As a PhD student at the Technische Universiteit Eindhoven I have two primary objectives: learning as much as possible and making a meaningful contribution. Oh, perhaps a third objective is also in order: earning a PhD from this wonderful institution. Additionally, I wish to experience the various cultures to be found in and around the university and I want to learn to speak Dutch properly. I look forward to the many interesting discussions we are bound to have here and perhaps even working together in the not too distant future.

Colloquium IS Mohammad Rasouli

Mohammad Rasouli

Information quality in dynamic networked business process management


The competition in globalized markets forces organizations to provide mass-customized integrated solutions for customers. Mass-customization of integrated solutions by business network requires adaptive interactions between parties to address emerging requirements of customers. These adaptive interactions need to be enabled by dynamic networked business processes (DNBP) that are supported by high quality information. However, the dynamic collaboration between parties can result in information quality (IQ) issues such as information syntactic and semantic misalignment,information leakage, and unclear information ownership. To counter negative consequences of poor IQ on the performance, the orchestrator of business network needs to clearly recognize these IQ issues. In this paper, we develop and evaluate a framework to address potential IQ issues related to DNBP. The development of the framework is based on a three step methodology that includes the characterization of dynamism of networked business processes, the characterization of IQ dimensions, and the exploration of IQ issues. To evaluate the practical significance of the explored IQ issues, we conduct a case study in a service ecosystem that is formed by a car leasing organization to provide integrated mobility solutions for customers.

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Colloquium IS Uzay Kaymak: A Discussion on Process Mining in Healthcare

Uzay Kaymak

A Discussion on Process Mining in Healthcare

In recent years, process mining has been studied for a number of applications in the healthcare domain. With the increasing need to bring healthcare processes better under control, such applications have significant potential to increase the share of process-oriented care delivery. Despite the potential, the usability of the method has been limited, mainly due to the overly complex models, which have been obtained in the healthcare settings. The complexity of these models is often attributed to the complexity of the health care domain. In this presentation, we will discuss whether this is sufficient explanation and argue that many process mining methods fail to identify good process models, even for well-defined clinical environments. We identify a number of reasons for this shortcoming and discuss a few challenges for future research in process mining in healthcare.

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New Employee: Rodrigo Gonçalves

RodrigoI will have my Integrated Master’s Degree in Mechanical Engineering at ‘Instituto Superior Técnico’ in Lisbon (Portugal) by the end of September, 2015. The Master is specialized in the fields of optimization, automation and control of mechanical systems thus I have a strong background in areas such as artificial intelligence, neural networks, fuzzy systems and optimal control. I also did a one-semester international exchange with Aarhus University (Denmark) from August 2013 until February 2014, where I gained an education in a new and different way.

The aim of my MSc thesis was to provide an analysis and methodology on how to proceed when facing transportation logistic problems with high complexity. During my thesis, I faced the current huge breakdown that exists in between transportation execution and planning. At Eindhoven University of Technology (TU/e), I will continue my research in the filed that I have been developing during my Master thesis. I will work as an employee of research & education in the Industrial & Innovation Sciences department.

New Employee: Bambang Suratno

bambangI took Industrial Engineering major for my bachelor degree in 2007 from Telkom University, Indonesia. After experiencing working as lead researcher in NGO and consulting firm for 4 years, I continued my study and did my master degree in 2013 majoring Industrial Engineering and Management at Bandung Institute of Technology, Indonesia. My research is in the utilization of enterprise information system for managerial benefit and explore its potential for developing knowledge management.

Currently, I am a pursuing my PhD in the Information Systems subdepartment of the School of Industrial Engineering, department of Industrial Engineering and Innovation Science, TU Eindhoven, Netherland. The general research domain of my PhD project will be ‘Support for Virtual Enterprises’, possibly covering elements from Information System Engineering, Business Process management, and Business Engineering.