Course paper / final thesis

Visualizing Uncertainty in Graphs

Starting date

1 July 2022

Duration of contract

3-6 months

Type of employment


The Scientific Visualization group at the Institute for Software Technology creates interactive and intuitive visualization applications for end-users and other stakeholders. Data that we receive from variant sources, e.g. simulation studies, sensors etc., comes with its own set of uncertainty. Uncertainty is the possibility that the observed data or model predictions could take on a set of possible values. The term risk is also used for those uncertainties where the potential error and bias remain unquantified. Therefore, it is critical that the methods we use to visualize this data must effectively calculate and show the underlying uncertainty or risk. Moreover, the relevance and meaning of uncertainty are different for different users (for example, domain experts, politicians, and laymen). Therefore, the solution should be applicable to the broad range of users.

Numerous studies have investigated and developed various uncertainty visualization techniques. For example, different graphical annotations: error plots, violin plots, quantile dot plots, and different visual encodings: fuzziness, arrangements, size, transparency etc. and other uncertainty visualization techniques like animations, contours etc. However, many of these techniques are not applicable as the size of the data increases or the domain of the data changes. We receive a large amount of data of spatio-temporal nature. Using simple uncertainty charts, for example violin charts for extensive timelines leading to many years, is very ineffective due to the clutter it brings, which impedes its readability.

In the scope of this thesis, different uncertainty visualizations, primarily of big spatio-temporal simulation data, needs to be investigated. Then applicable techniques need to be implemented or developed if no suitable technique exists. This thesis will investigate many of the following questions in the broader sense. However, this is not an exclusive task list and the student can form its own research directions.

  • understanding of uncertainty in temporal data
  • What are the different aspects of uncertainty in temporal data that can affect its analysis? Also, the same with the simulation of the temporal data.
  • How these aspects were addressed in the previous studies, and what are the techniques developed?
  • Implementation of the techniques on our datasets and doing user studies. If no suitable technique exists, develop your own.

If you enjoy visualizations and are interested in this thesis, we are looking forward to receiving your application. We are also welcome to discuss any innovative ideas that matches to the scope of this thesis. You can start immediately.

Your qualifications:

  • pursuing bachelor or master degree in Computer Science or similar
  • good programing skills in JavaScript, D3.js, Typescript
  • understanding of web development frameworks like React or Angular is a plus
  • knowledge of Python is plus

Your benefits:

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x). Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

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Technical contact

Jonas Gilg
Institute for Software Technology

Phone: +49 531 295-2221

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Vacancy 68767

HR department Braunschweig

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DLR site Braunschweig

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DLR Institute for Software Technology

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