Anika Seufert - Lehrstuhl für Informatik III

Telefon	(0931) 31-89033
Telefax	(0931) 31-86632
E-Mail	anika.seufert@informatik.uni-wuerzburg.de
Raum	A212
Anschrift	Lehrstuhl für Informatik III Am Hubland D-97074 Würzburg

Inhalt: Übersicht | Veröffentlichungen | Lehre | Arbeitsgruppe | Projekte

Forschungsinteressen

Meine Forschungsinteressen umfassen die folgenden Themen:

Quality of Experience (QoE) von Internet-Anwendungen (z. B. Video-/Musik-Streaming-Apps, mobile Messaging-Apps)
Messungen, Analyse und Modellierung des Netzwerkverkehrs
QoE-bewusste Lösungen zur Verwaltung des Netzwerkverkehrs
Leistungsbewertung und Modellierung von Kommunikationssystemen
Gruppenbasierte Kommunikation

Neueste Veröffentlichungen

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 = 6, 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 = 3, 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.
Waiting along the Path: How Browsing Delays Impact the QoE of Music Streaming Applications. Seufert, Anika; Schweifler, Ralf; Poignée, Fabian; Seufert, Michael; Hoßfeld, Tobias. In 14th International Conference on Quality of Multimedia Experience (QoMEX). 2022.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ Download ]
Streaming has become the dominant source of media consumption, which not only applies to the widely researched field of video streaming, but also to music streaming. Here, previous studies so far have only researched the impact of streaming aspects, such as stalling events or initial loading times, on the QoE of music streaming. However, when using a music streaming application, users are already facing waiting times along the click path before they can start the actual streaming. These waiting times are caused by browsing delays, e.g., during searching for songs or scrolling through playlists, and can potentially deteriorate the QoE of the music streaming application. In this work, we conduct an online QoE study to quantify the impact of these browsing delays with the support of an emulated mobile music streaming web app. We found that browsing delays have no impact on the music streaming QoE, which shows that users are able to clearly distinguish between the two main functionalities of such apps, namely, browsing and streaming. However, browsing delays significantly reduce the QoE of the entire music streaming application, to a similar extent as if QoE degradations happen during the actual streaming. This shows that both browsing and streaming are equally important and have to be considered when designing music streaming applications.

@inproceedings{info3-inproceedings-2022-13, abstract = {Streaming has become the dominant source of media consumption, which not only applies to the widely researched field of video streaming, but also to music streaming. Here, previous studies so far have only researched the impact of streaming aspects, such as stalling events or initial loading times, on the QoE of music streaming. However, when using a music streaming application, users are already facing waiting times along the click path before they can start the actual streaming. These waiting times are caused by browsing delays, e.g., during searching for songs or scrolling through playlists, and can potentially deteriorate the QoE of the music streaming application. In this work, we conduct an online QoE study to quantify the impact of these browsing delays with the support of an emulated mobile music streaming web app. We found that browsing delays have no impact on the music streaming QoE, which shows that users are able to clearly distinguish between the two main functionalities of such apps, namely, browsing and streaming. However, browsing delays significantly reduce the QoE of the entire music streaming application, to a similar extent as if QoE degradations happen during the actual streaming. This shows that both browsing and streaming are equally important and have to be considered when designing music streaming applications.}, author = {Seufert, Anika and Schweifler, Ralf and Poignée, Fabian and Seufert, Michael and Hoßfeld, Tobias}, booktitle = {14th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {sys:hidden:inproceedings}, month = 9, title = {Waiting along the Path: How Browsing Delays Impact the QoE of Music Streaming Applications}, year = 2022 }

%0 Conference Paper %1 info3-inproceedings-2022-13 %A Seufert, Anika %A Schweifler, Ralf %A Poignée, Fabian %A Seufert, Michael %A Hoßfeld, Tobias %B 14th International Conference on Quality of Multimedia Experience (QoMEX) %D 2022 %T Waiting along the Path: How Browsing Delays Impact the QoE of Music Streaming Applications %X Streaming has become the dominant source of media consumption, which not only applies to the widely researched field of video streaming, but also to music streaming. Here, previous studies so far have only researched the impact of streaming aspects, such as stalling events or initial loading times, on the QoE of music streaming. However, when using a music streaming application, users are already facing waiting times along the click path before they can start the actual streaming. These waiting times are caused by browsing delays, e.g., during searching for songs or scrolling through playlists, and can potentially deteriorate the QoE of the music streaming application. In this work, we conduct an online QoE study to quantify the impact of these browsing delays with the support of an emulated mobile music streaming web app. We found that browsing delays have no impact on the music streaming QoE, which shows that users are able to clearly distinguish between the two main functionalities of such apps, namely, browsing and streaming. However, browsing delays significantly reduce the QoE of the entire music streaming application, to a similar extent as if QoE degradations happen during the actual streaming. This shows that both browsing and streaming are equally important and have to be considered when designing music streaming applications.
Pandemic in the Digital Age: Analyzing WhatsApp Communication Behavior before, during, and after the COVID-19 Lockdown. Seufert, Anika; Poignée, Fabian; Hoßfeld, Tobias; Seufert, Michael. In Humanities and Social Sciences Communications, 9(1), pp. 140 (1–9). 2022.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
The strict restrictions introduced by the COVID-19 lockdowns, which started from March 2020, changed people’s daily lives and habits on many different levels. In this work, we investigate the impact of the lockdown on the communication behavior in the mobile instant messaging application WhatsApp. Our evaluations are based on a large dataset of 2577 private chat histories with 25,378,093 messages from 51,973 users. The analysis of the one-to-one and group conversations confirms that the lockdown severely altered the communication in WhatsApp chats compared to pre-pandemic time ranges. In particular, we observe short-term effects, which caused an increased message frequency in the first lockdown months and a shifted communication activity during the day in March and April 2020. Moreover, we also see long-term effects of the ongoing pandemic situation until February 2021, which indicate a change of communication behavior towards more regular messaging, as well as a persisting change in activity during the day. The results of our work show that even anonymized chat histories can tell us a lot about people’s behavior and especially behavioral changes during the COVID-19 pandemic and thus are of great relevance for behavioral researchers. Furthermore, looking at the pandemic from an Internet provider perspective, these insights can be used during the next pandemic, or if the current COVID-19 situation worsens, to adapt communication networks to the changed usage behavior early on and thus avoid network congestion.

@article{info3-article-2022-14, abstract = {The strict restrictions introduced by the COVID-19 lockdowns, which started from March 2020, changed people’s daily lives and habits on many different levels. In this work, we investigate the impact of the lockdown on the communication behavior in the mobile instant messaging application WhatsApp. Our evaluations are based on a large dataset of 2577 private chat histories with 25,378,093 messages from 51,973 users. The analysis of the one-to-one and group conversations confirms that the lockdown severely altered the communication in WhatsApp chats compared to pre-pandemic time ranges. In particular, we observe short-term effects, which caused an increased message frequency in the first lockdown months and a shifted communication activity during the day in March and April 2020. Moreover, we also see long-term effects of the ongoing pandemic situation until February 2021, which indicate a change of communication behavior towards more regular messaging, as well as a persisting change in activity during the day. The results of our work show that even anonymized chat histories can tell us a lot about people’s behavior and especially behavioral changes during the COVID-19 pandemic and thus are of great relevance for behavioral researchers. Furthermore, looking at the pandemic from an Internet provider perspective, these insights can be used during the next pandemic, or if the current COVID-19 situation worsens, to adapt communication networks to the changed usage behavior early on and thus avoid network congestion.}, author = {Seufert, Anika and Poignée, Fabian and Hoßfeld, Tobias and Seufert, Michael}, journal = {Humanities and Social Sciences Communications}, keywords = {ucn}, month = 4, number = 1, pages = {140 (1-9)}, title = {Pandemic in the Digital Age: Analyzing WhatsApp Communication Behavior before, during, and after the COVID-19 Lockdown}, volume = 9, year = 2022 }

%0 Journal Article %1 info3-article-2022-14 %A Seufert, Anika %A Poignée, Fabian %A Hoßfeld, Tobias %A Seufert, Michael %D 2022 %J Humanities and Social Sciences Communications %N 1 %P 140 (1-9) %T Pandemic in the Digital Age: Analyzing WhatsApp Communication Behavior before, during, and after the COVID-19 Lockdown %V 9 %X The strict restrictions introduced by the COVID-19 lockdowns, which started from March 2020, changed people’s daily lives and habits on many different levels. In this work, we investigate the impact of the lockdown on the communication behavior in the mobile instant messaging application WhatsApp. Our evaluations are based on a large dataset of 2577 private chat histories with 25,378,093 messages from 51,973 users. The analysis of the one-to-one and group conversations confirms that the lockdown severely altered the communication in WhatsApp chats compared to pre-pandemic time ranges. In particular, we observe short-term effects, which caused an increased message frequency in the first lockdown months and a shifted communication activity during the day in March and April 2020. Moreover, we also see long-term effects of the ongoing pandemic situation until February 2021, which indicate a change of communication behavior towards more regular messaging, as well as a persisting change in activity during the day. The results of our work show that even anonymized chat histories can tell us a lot about people’s behavior and especially behavioral changes during the COVID-19 pandemic and thus are of great relevance for behavioral researchers. Furthermore, looking at the pandemic from an Internet provider perspective, these insights can be used during the next pandemic, or if the current COVID-19 situation worsens, to adapt communication networks to the changed usage behavior early on and thus avoid network congestion.
Delivering User Experience over Networks: Towards a Quality of Experience Centered Design Cycle for Improved Design of Networked Applications. Seufert, Anika; Schröder, Svenja; Seufert, Michael. In SN Computer Science, 2. 2021.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ Download ]
To deliver the best user experience (UX), the human-centered design cycle (HCDC) serves as a well-established guideline to application developers. However, it does not yet cover network-specific requirements, which become increasingly crucial, as most applications deliver experience over the Internet. The missing network-centric view is provided by Quality of Experience (QoE), which could team up with UX towards an improved overall experience. By considering QoE aspects during the development process, it can be achieved that applications become network-aware by design. In this paper, the Quality of Experience Centered Design Cycle (QoE-CDC) is proposed, which provides guidelines on how to design applications with respect to network-specific requirements and QoE. Its practical value is showcased for popular application types and validated by outlining the design of a new smartphone application. We show that combining HCDC and QoE-CDC will result in an application design, which reaches a high UX and avoids QoE degradation.

@article{info3-article-2021-6, abstract = {To deliver the best user experience (UX), the human-centered design cycle (HCDC) serves as a well-established guideline to application developers. However, it does not yet cover network-specific requirements, which become increasingly crucial, as most applications deliver experience over the Internet. The missing network-centric view is provided by Quality of Experience (QoE), which could team up with UX towards an improved overall experience. By considering QoE aspects during the development process, it can be achieved that applications become network-aware by design. In this paper, the Quality of Experience Centered Design Cycle (QoE-CDC) is proposed, which provides guidelines on how to design applications with respect to network-specific requirements and QoE. Its practical value is showcased for popular application types and validated by outlining the design of a new smartphone application. We show that combining HCDC and QoE-CDC will result in an application design, which reaches a high UX and avoids QoE degradation.}, author = {Seufert, Anika and Schröder, Svenja and Seufert, Michael}, journal = {SN Computer Science}, keywords = {sys:hidden:article}, month = 9, title = {Delivering User Experience over Networks: Towards a Quality of Experience Centered Design Cycle for Improved Design of Networked Applications}, volume = 2, year = 2021 }

%0 Journal Article %1 info3-article-2021-6 %A Seufert, Anika %A Schröder, Svenja %A Seufert, Michael %D 2021 %J SN Computer Science %T Delivering User Experience over Networks: Towards a Quality of Experience Centered Design Cycle for Improved Design of Networked Applications %V 2 %X To deliver the best user experience (UX), the human-centered design cycle (HCDC) serves as a well-established guideline to application developers. However, it does not yet cover network-specific requirements, which become increasingly crucial, as most applications deliver experience over the Internet. The missing network-centric view is provided by Quality of Experience (QoE), which could team up with UX towards an improved overall experience. By considering QoE aspects during the development process, it can be achieved that applications become network-aware by design. In this paper, the Quality of Experience Centered Design Cycle (QoE-CDC) is proposed, which provides guidelines on how to design applications with respect to network-specific requirements and QoE. Its practical value is showcased for popular application types and validated by outlining the design of a new smartphone application. We show that combining HCDC and QoE-CDC will result in an application design, which reaches a high UX and avoids QoE degradation.

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Anika Seufert M. Sc. (geb. Schwind)

Forschungsinteressen

Neueste Veröffentlichungen

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