Anika Seufert - Chair of Computer Science 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

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.
Share and Multiply: Modeling Communication and Generated Traffic in Private WhatsApp Groups. Seufert, Anika; Poignée, Fabian; Seufert, Michael; Hoßfeld, Tobias. In IEEE Access, 11, pp. 25401–25414. 2023.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group.

@article{info3-article-2023-2, abstract = {Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group.}, author = {Seufert, Anika and Poignée, Fabian and Seufert, Michael and Hoßfeld, Tobias}, journal = {IEEE Access}, keywords = {ucn}, month = {03}, pages = {25401 - 25414}, title = {Share and Multiply: Modeling Communication and Generated Traffic in Private WhatsApp Groups}, volume = 11, year = 2023 }

%0 Journal Article %1 info3-article-2023-2 %A Seufert, Anika %A Poignée, Fabian %A Seufert, Michael %A Hoßfeld, Tobias %D 2023 %J IEEE Access %P 25401 - 25414 %T Share and Multiply: Modeling Communication and Generated Traffic in Private WhatsApp Groups %V 11 %X Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group.
Industrial User Experience Index vs. Quality of Experience Models. Hoßfeld, Tobias; Seufert, Anika; Loh, Frank; Wunderer, Stefan; Davies, John. In IEEE Communications Magazine, 61(1), pp. 98–104. 2023.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Network operators have increased interest in understanding and quantifying the user perceived quality of experience (QoE). Appropriate QoE models allow mapping of measurable QoS parameters to QoE metrics like mean opinion scores (MOS) or poor-or-worse (PoW) ratios. Unfortunately, there are no QoE models for all applications available yet, and development and standardization is rather time-consuming. For that reason, industry often implements simple but effective approaches based on expert knowledge to get a user experience index able to deliver high value insights. This index is often defined based on thresholds of measurable QoS parameters to classify the resulting user experience. The question arises whether such a threshold-based user experience index can properly estimate the QoE in a system. We therefore answer in this work whether the user experience index is able to quantify the expected QoE in a system and if other QoE metrics like PoW ratios can be derived. Furthermore, we discuss the interpretation and the limits of the index. As a key contribution, we present a detailed systematic analysis of the threshold-based QoE concept and discuss its usability as QoE estimation methodology for industry.

@article{info3-article-2023-7, abstract = {Network operators have increased interest in understanding and quantifying the user perceived quality of experience (QoE). Appropriate QoE models allow mapping of measurable QoS parameters to QoE metrics like mean opinion scores (MOS) or poor-or-worse (PoW) ratios. Unfortunately, there are no QoE models for all applications available yet, and development and standardization is rather time-consuming. For that reason, industry often implements simple but effective approaches based on expert knowledge to get a user experience index able to deliver high value insights. This index is often defined based on thresholds of measurable QoS parameters to classify the resulting user experience. The question arises whether such a threshold-based user experience index can properly estimate the QoE in a system. We therefore answer in this work whether the user experience index is able to quantify the expected QoE in a system and if other QoE metrics like PoW ratios can be derived. Furthermore, we discuss the interpretation and the limits of the index. As a key contribution, we present a detailed systematic analysis of the threshold-based QoE concept and discuss its usability as QoE estimation methodology for industry.}, author = {Hoßfeld, Tobias and Seufert, Anika and Loh, Frank and Wunderer, Stefan and Davies, John}, journal = {IEEE Communications Magazine}, keywords = {5gqmon}, month = {01}, number = 1, pages = {98-104}, title = {Industrial User Experience Index vs. Quality of Experience Models}, volume = 61, year = 2023 }

%0 Journal Article %1 info3-article-2023-7 %A Hoßfeld, Tobias %A Seufert, Anika %A Loh, Frank %A Wunderer, Stefan %A Davies, John %D 2023 %J IEEE Communications Magazine %N 1 %P 98-104 %T Industrial User Experience Index vs. Quality of Experience Models %V 61 %X Network operators have increased interest in understanding and quantifying the user perceived quality of experience (QoE). Appropriate QoE models allow mapping of measurable QoS parameters to QoE metrics like mean opinion scores (MOS) or poor-or-worse (PoW) ratios. Unfortunately, there are no QoE models for all applications available yet, and development and standardization is rather time-consuming. For that reason, industry often implements simple but effective approaches based on expert knowledge to get a user experience index able to deliver high value insights. This index is often defined based on thresholds of measurable QoS parameters to classify the resulting user experience. The question arises whether such a threshold-based user experience index can properly estimate the QoE in a system. We therefore answer in this work whether the user experience index is able to quantify the expected QoE in a system and if other QoE metrics like PoW ratios can be derived. Furthermore, we discuss the interpretation and the limits of the index. As a key contribution, we present a detailed systematic analysis of the threshold-based QoE concept and discuss its usability as QoE estimation methodology for industry.

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

Anika Seufert M. Sc. (née Schwind)

Research Interests

Recent Publications

Data privacy protection

Data privacy protection

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