Distributed Systems > CS > JBI > FWN > RUG

Available Projects



Many projects can be adjusted so that they fit the constraints of a Master's, term, a Bachelor project, or an internship. Although some projects may have been available for some time, this does not mean they have become less relevant.


Internal Projects

  • Verification of Service Compositions. Originally designed to support rigid repetitive units of work, business processes currently are required to support flexible and variable processes implemented as service compositions. These flexible compositions, however, must remain true to its initial process requirements and business rules. We developed a Java package that uses a model checking approach to verify the compliance of compositions against sets of formal specifications. The package takes a Petri net and a set of specifications as input, internally converts and optimizes the composition to a verifiable model, verifies the set of specifications against the model, and returns the results of verification. The package is included in the Apromore process analytics platform as a plugin. In this context, the following assignments are available:
    1. Implement BPMN file format to Petri net PNML file format conversion (BSc).
    2. Implement WS-BPEL file format to Petri-net PNML file format conversion (BSc).
    3. Develop direct support for BPMN file format verification (BSc/int).
    4. Develop direct support for WS-BPEL file format verification (BSc/int).
    5. Develop an internal verification algorithm (MSc).
    6. Develop a visual specification design plugin for Apromore (MSc).
    Contact: Heerko.
  • OnlineSuperLearner - large scale machine learning. The Distributed Systems group (University of Groningen), Developmental Psychology (University of Groningen), and MAP5 (Université Paris Descartes) are collaborating on a new, scalable implementation of the so-called Online SuperLearner. With this meta machine learning algorithm we aim to improve clinical decision making and psychopathology research. The Master's student involved in this project will help set up a scalable architecture, capable of dealing with tremendous amounts of data. Technologies of interest are, for example, Spark, Scala, Kafka, Hadoop, and many others. More information: doc. Contact: Frank Blaauw.
  • Machine learning for the Dutch special forces. The special forces of the Royal Netherlands Army (Korps Commandotroepen) is tasked with special operations that can take place anywhere in the world under any circumstances. For the special forces two questions are key: How should we select operators (commandos)? And how can we monitor their psychological and physiological states? With new technologies such as wearable sensors it is possible to track performances of the operators. The challenge from a computer science perspective is how to use techniques such as machine learning to detect what makes operators successful, and to better understand fluctuations in their psychological and physiological fitness. In this unique project based on a collaboration between the special forces and the University of Groningen, you will contribute to meeting this challenge. Contact: Frank or Ando.
  • Improve the performance of the national speed skaters. Are you a fan of speed skating and machine learning? Then this project is the perfect fit for you. In a collaboration between KPN, Thialf, and the University of Groningen, we offer a research and development position (Master's internship, or Master's thesis) in which you are involved in the creation of prediction models for predicting the perfect race. In this project you will work with various interesting techniques, such as machine learning, targeted learning, and statistical inference. From a technological point of view this project is highly interesting and relevant. It builds on a highly scalable, distributed platform called the KPN Data Service hub. Contact: Frank or Ando.
  • Highly distributed in-browser computing. Large scale distributed computing is a necessity for many companies nowadays. The huge data centers of these companies have to process large amounts of data, but also need to deal with constantly failing nodes. Fortunately, dealing with failing and joining nodes is a problem solved ages ago. However, what if you would push this flexible architectural paradigm of joining and leaving to the limit? In this project the student is asked to create a system JavaScript based distributed computing system that runs in the browser. The separate browser (and NodeJS) clients should be managed through one, or a series of master nodes and should be able to make computations for them in a reliable and secure way. The student will implement this system and demonstrate the workings with a simple task that is distributed over a network of failing and joining nodes. Contact: Frank Blaauw.
  • Less-intrusive Context-Aware System. In this project, we are interested in collecting precise and fine-grain human context in a building. The context could be users’ -location, -occupancy, or -activity. Such information is required by, for example, smart or intelligent building, as a foundation to decide an action to achieve predetermined goals, such as maximizing energy saving while less compromising with user comfort. Off-the-shelf sensors that are less intrusive are preferable to work with. Some topics are available such as (but are not limited to): 1) the exploration of sensor features; 2) sensor fusion; 3) user behavior or device utilization pattern recognition. Furthermore, how the proposed system would be scalable and portable is also interesting to be explored more. Contact: Azkario or Alexander.
  • Full-stack developer position for the development of a mental health platform. We are looking for a full-stack developer to start-up a four-year research project at the department of developmental psychology (RUG). This research project will examine the dynamics of well-being and psychological distress in children and adolescents using both cross-sectional and diary assessments. The first phase of this project will focus on the perspectives of the parents while the second phase will focus on the perspectives of the children and adolescents themselves. The master student involved in this project will be responsible for creating a user-friendly, visually attractive, and scalable Web platform used in the first phase of the project (i.e., focusing on the parents). Via this platform we wish to (1) implement the relevant questionnaires to parents and (2) provide personalized feedback. The technologies we use include (but are not limited to): Ruby on Rails, ReactJS, and Service Oriented Architectures. The master student will collaborate with four researchers from developmental psychology and computer science. The project will start at the end of January/ beginning of February. Contact: Frank or Ando.
  • Energy future decision tool. The Netherlands are committed to reducing their greenhouse gas emissions by at least 80% by 2050, relative to 1990 levels. This demands a transition to more sustainable ways of generating and using energy. There are many different pathways how this can be done and the Dutch society will have to decide upon in the coming years. To give people a better understanding of the required decisions and to help us learn about how people decide on the energy future and which information they take into consideration, you will develop a tool (similar to the 2050 calculator tool) which simulates the Dutch energy transition and exposes people to the technology choices and the necessary tradeoffs. Contact: Alexander or Nadja Contzen.
  • Energy-efficient Data Centers Models. Decreasing energy consumption in data center is a very important topic nowadays. This MSc project will focus on translating key aspects of data center operation to workable data center models. The project features a collaboration with Target Holding/CIT, who manage the university data center. In this project you will discuss with data center operators to identify operational processes and key parameters, and then translate those into tools that can be used for predicting and modeling data center behavior. As such this is a unique opportunity to get a look behind the scenes of data center operation. For this project you will cooperate with the SMS-ENTEG group. More info: (pdf) Contact: Alexander or Tobias van Damme.
  • Sustainable Data Centers. In the context of a regional project in collaboration with KPN and an international project with Cognizant India, the research aims at studying techniques to save energy in modern data centres. Internet of things and machine learning are central to the approach. In particular, the project will involve one or more of the following items: *) environmental model of data center for steering/controlling energy consumption (preferably generalisable); *) energy consumption model of a data center and its components; *) report containing recommendations for reducing carbon-dioxide footprints of datacenter; *) adaptive planning and scheduling techniques to save energy in data centres. Contact: Alexander or Wico Mulder.
  • Optimization of integrated energy flow. The objective of the MERGE project is to study and develop an energy management system to promote the integration of different energy systems, mainly electricity and gas natural, at the level of the distribution network. The physical properties of energy carriers and the complexity of different infrastructures have to be taken into account while investigating the dynamic behaviour of the integrated grid. The proposed MSc project is about developing a program to optimize the flow of gas and electricity over an integrated distribution network, described by non-linear functions. The stages of the project include modeling the gas-electricity flow as a non-linear system, presenting an overview and comparison of available toolbox and libraries (for example, MATLAB toolboxes, C++ or Java libraries), developing of a program to optimize electricity and gas flows on scalable networks. Contact: Laura.
  • Distributed Discrete Optimisation. Constraint satisfaction problems are a type of search problem with a broad range of applications, including planning, scheduling and resource allocation. Solving these problems with respect to a certain objective function allows optimisation of that particular problem, for example, optimising the energy consumption of a building. Unfortunately, this problem is NP-hard, meaning that algorithms that are guaranteed to find the optimal solution to a constraint satisfaction problem require exponential time to do so. Consequently, the size that algorithms can handle is limited (e.g. constructing a CSP to model an entire building would be impossible). When dealing with dynamic environments, the problem also has to be solved continuously and possibly in real time, requiring a solution to be available within a limited amount of time. Constraint networks of real-world problems are often sparse, however, and if the problem domain exhibits inherent locality, large-scale problems can be solved more efficiently by exploiting these structures (e.g. processes within a building are often mostly localised within a single room or area). This relative independence also facilitates parallelism in the search process, allowing a distributed cluster of machines to solve the problem faster and enables scaling with respect to the problem size. Many projects related to this topic are available, such as realising a more efficient distributed search algorithm, dealing with dynamicity within the environment by continuously solving the problem, increasing the level of parallelism of the algorithm and more. Contact: Michel Medema.



External Projects


  • Measures of Social Behaviour. Questionnaires are sensitive to several sources of noise. And above all, the moment-by-moment quantification of behaviour is impossible while using questionnaires. To manoeuvre away from these deficiencies we have developed a passive monitoring system that is based on the ubiquity smartphone technology. Due to the advances in technology, the World Economic Forum announced in February 2016, that the world is entering its Fourth Industrial Revolution based on hyper-connectivity, data-driven solutions and artificial intelligence (World Economic Forum, 2016). hyper-connectivity is characterised by a state of being constantly connected to individuals and machines through devices such as smartphones. hyper-connectivity and large-scale data collection through smartphones are the fundamental elements of new technological initiatives in healthcare and biomedical-research. These smartphone-based technological initiatives are largely due to the fact that the number of sensors embedded in smartphones have exploded over the past few years. Nowadays the majority of smartphones are equipped with sensors such as a GPS, accelerometer, gyroscope, WIFI, bluetooth, camera and microphone. These smartphones aggregate a large amount of user related data which are in the context of research largely untouched. Our ambition is to develop several objective measures of social behaviour by using the data collected through our passive monitoring application. The objective quantification of social behaviour is important since the majority of psychiatric disorders affect social behaviour. In the context of a master thesis, we would like a master student with good knowledge of R to develop several of these measures that are related to social behaviour and test these measures on data of psychiatric patients. Contact: Niels Jongs
  • Passive Behavioural Monitoring. Advances in low power communication technologies and large scale data processing continue to give rise to the concept of mobile healthcare systems as an integral part of clinical care/research processes. This project will focus on the data that is collected by a passive behavioural monitoring system in which personal mobile devices are used as a measuring instrument. The data mainly consists of sensor and activity data which might allow us to differentiate between healthy and non-healthy individuals. In this project, our aim is to establish behavioural profiles which are related to neuropsychiatric disorders by using advanced data analysis and data mining techniques. These behavioural profiles are derived from the sensor and activity data collected from a passive behavioural monitoring system and are used to predict the onset or relapse of neuropsychiatric disorders. Additionally, our aim is translate these behavioural profiles to animal behavioural models of which the data is collected in a controlled lab environment. Contact: Martrien Kas.
  • Automated tool for security checks. Cedel BV in Assen is a Small Enterprise with the main focus on engineering in the domain of sustainable energy. They developed the CEMM (Cedel Energy Monitor Management system), in three series: CEMM, CEMM plus and the CEMM pro. These product give the user inside knowledge about their energy use: Smart meter, PV-panels, Heat pomp, Solvena roof, etc. Characteristics of the CEMM: ARM processor, data is stored locally on the device (No cloud computing), webserver to show information to the user. Cloud portal (vps) is used between user and CEMM to access information (example: http://demo.cedel.nl). Cedel has an assignment for a bachelor or master student, RuG Computer Science, who likes to work in Embedded Systems environment for a placement. Cedel wants to know to which level the CEMM is secure. Objective: Development of an automated tool to check the security level of the CEMM and advise them how to improve the CEMM. Contact: Remco de Vries or Peter Kamphuis.
  • Flexible computing infrastructures (proposed by TNO Groningen). More information: pdf. Contact: Alexander or TNO directly (contact details in the PDF).
  • Privacy-friendly context-aware services (proposed by TNO Groningen). More information: pdf. Contact: Alexander or TNO directly (contact details in the PDF).
  • Interaction with devices in a household for the purpose of enabling smart grid services (proposed by TNO Groningen). More information: pdf. Contact: Alexander or TNO directly (contact details in the PDF).