Anika Seufert - Lehrstuhl für Informatik III

Phone	+49 0931 31-89033
Telefax	+49 0931 31-86632
E-Mail	anika.seufert@informatik.uni-wuerzburg.de
Room	A212
Address	Lehrstuhl für Informatik III Am Hubland 97074 Würzburg Germany

Contents: Overview | Publications | Teaching | Working Group | Projects

Research Interests

My research interests include the following topics:

Quality of Experience (QoE) of Internet applications (e.g., video/music streaming apps, mobile messaging apps)
Measurements, analysis, and modeling of network traffic
QoE-aware network traffic management solutions
Performance evaluation and modeling of communication systems
Group-based communication

Recent Publications

A Tutorial on Data-Driven Quality of Experience Modeling with Explainable Artificial Intelligence. Wehner, Nikolas; Seufert, Anika; Hoßfeld, Tobias; Seufert, Michael. In IEEE Communications Surveys & Tutorials. 2025.
- [ BibTeX ]
- [ EndNote ]
@article{info3-article-2025-2, author = {Wehner, Nikolas and Seufert, Anika and Hoßfeld, Tobias and Seufert, Michael}, journal = {IEEE Communications Surveys & Tutorials}, keywords = {sys:hidden:article}, month = {06}, title = {A Tutorial on Data-Driven Quality of Experience Modeling with Explainable Artificial Intelligence}, year = 2025 }

%0 Journal Article %1 info3-article-2025-2 %A Wehner, Nikolas %A Seufert, Anika %A Hoßfeld, Tobias %A Seufert, Michael %D 2025 %J IEEE Communications Surveys & Tutorials %T A Tutorial on Data-Driven Quality of Experience Modeling with Explainable Artificial Intelligence
Measuring and Improving the Quality of Experience of Trending Internet Applications in Mobile Networks. Technical Report (PhD dissertation), . Seufert, Anika. PhD dissertation. University of Würzburg, 2025, January.
- [ BibTeX ]
- [ EndNote ]
@phdthesis{info3-dissertation-2025-1, author = {Seufert, Anika}, keywords = {sys:hidden:supervisor:tobias.hoßfeld}, month = {01}, school = {University of Würzburg}, title = {Measuring and Improving the Quality of Experience of Trending Internet Applications in Mobile Networks}, year = 2025 }

%0 Thesis %1 info3-dissertation-2025-1 %A Seufert, Anika %D 2025 %T Measuring and Improving the Quality of Experience of Trending Internet Applications in Mobile Networks
Fitting the Puzzle: Towards Source Traffic Modeling For Mobile Instant Messaging. Poignée, Fabian; Seufert, Anika; Loh, Frank; Seufert, Michael; Hoßfeld, Tobias. In 2024 15th International Conference on Network of the Future (NoF). Castelldefels (Barcelona), Spain, 2024.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{info3-inproceedings-2024-11, address = {Castelldefels (Barcelona), Spain}, author = {Poignée, Fabian and Seufert, Anika and Loh, Frank and Seufert, Michael and Hoßfeld, Tobias}, booktitle = {2024 15th International Conference on Network of the Future (NoF)}, keywords = {ucn}, month = 10, title = {Fitting the Puzzle: Towards Source Traffic Modeling For Mobile Instant Messaging}, year = 2024 }

%0 Conference Paper %1 info3-inproceedings-2024-11 %A Poignée, Fabian %A Seufert, Anika %A Loh, Frank %A Seufert, Michael %A Hoßfeld, Tobias %B 2024 15th International Conference on Network of the Future (NoF) %C Castelldefels (Barcelona), Spain %D 2024 %T Fitting the Puzzle: Towards Source Traffic Modeling For Mobile Instant Messaging
Sitting, Chatting, Waiting: Influence of Loading Times on Mobile Instant Messaging QoE. Seufert, Anika; Baur, Carina; Poignée, Fabian; Seufert, Michael; Hoßfeld, Tobias. In 16th International Conference on Quality of Multimedia Experience (QoMEX). Karlshamn, Sweden, 2024.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
This paper explores the relationship between loading times and Quality of Experience (QoE) in Mobile Instant Messaging (MIM) applications. Using a web application that mimics MIM interfaces, we conducted a QoE study in which participants engaged with a virtual chat partner. We controlled image loading times during chatting and evaluated their impact on QoE, annoyance, and acceptance ratings. Although the results show no difference in the QoE ratings, they clearly show that longer delays lead to greater annoyance and lower user acceptance. These findings underscore the importance for MIM app providers to minimize loading times in order to increase user satisfaction and retention.

@inproceedings{info3-inproceedings-2024-5, abstract = {This paper explores the relationship between loading times and Quality of Experience (QoE) in Mobile Instant Messaging (MIM) applications. Using a web application that mimics MIM interfaces, we conducted a QoE study in which participants engaged with a virtual chat partner. We controlled image loading times during chatting and evaluated their impact on QoE, annoyance, and acceptance ratings. Although the results show no difference in the QoE ratings, they clearly show that longer delays lead to greater annoyance and lower user acceptance. These findings underscore the importance for MIM app providers to minimize loading times in order to increase user satisfaction and retention.}, address = {Karlshamn, Sweden}, author = {Seufert, Anika and Baur, Carina and Poignée, Fabian and Seufert, Michael and Hoßfeld, Tobias}, booktitle = {16th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {can}, month = {06}, title = {Sitting, Chatting, Waiting: Influence of Loading Times on Mobile Instant Messaging QoE}, year = 2024 }

%0 Conference Paper %1 info3-inproceedings-2024-5 %A Seufert, Anika %A Baur, Carina %A Poignée, Fabian %A Seufert, Michael %A Hoßfeld, Tobias %B 16th International Conference on Quality of Multimedia Experience (QoMEX) %C Karlshamn, Sweden %D 2024 %T Sitting, Chatting, Waiting: Influence of Loading Times on Mobile Instant Messaging QoE %X This paper explores the relationship between loading times and Quality of Experience (QoE) in Mobile Instant Messaging (MIM) applications. Using a web application that mimics MIM interfaces, we conducted a QoE study in which participants engaged with a virtual chat partner. We controlled image loading times during chatting and evaluated their impact on QoE, annoyance, and acceptance ratings. Although the results show no difference in the QoE ratings, they clearly show that longer delays lead to greater annoyance and lower user acceptance. These findings underscore the importance for MIM app providers to minimize loading times in order to increase user satisfaction and retention.
Explainable Data-Driven QoE Modelling using XAI. Wehner, Nikolas; Seufert, Anika; Hoßfeld, Tobias; Seufert, Michael. In 15th International Conference on Quality of Multimedia Experience (QoMEX). Ghent, Belgium, 2023.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eXplainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.

@inproceedings{info3-inproceedings-2023-4, abstract = {Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eXplainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.}, address = {Ghent, Belgium}, author = {Wehner, Nikolas and Seufert, Anika and Hoßfeld, Tobias and Seufert, Michael}, booktitle = {15th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {ucn}, month = {06}, title = {Explainable Data-Driven QoE Modelling using XAI}, year = 2023 }

%0 Conference Paper %1 info3-inproceedings-2023-4 %A Wehner, Nikolas %A Seufert, Anika %A Hoßfeld, Tobias %A Seufert, Michael %B 15th International Conference on Quality of Multimedia Experience (QoMEX) %C Ghent, Belgium %D 2023 %T Explainable Data-Driven QoE Modelling using XAI %X Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eXplainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.

A comprehensive list of all publications can be found here: Publications

Anika Seufert M. Sc. (née Schwind)

Research Interests

Recent Publications

Picture credits