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

Seufert, A., Schweifler, R., Poignée, F., Seufert, M., Hoßfeld, T.: Waiting along the Path: How Browsing Delays Impact the QoE of Music Streaming Applications. 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.
Seufert, A., Poignée, F., Hoßfeld, T., Seufert, M.: Pandemic in the Digital Age: Analyzing WhatsApp Communication Behavior before, during, and after the COVID-19 Lockdown. Humanities and Social Sciences Communications. 9, 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.
Seufert, A., Schröder, S., Seufert, M.: Delivering User Experience over Networks: Towards a Quality of Experience Centered Design Cycle for Improved Design of Networked Applications. SN Computer Science. 2, (2021).
- [ Abstract ]
- [ BibTeX ]
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- [ 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.
Wamser, F., Seufert, A., Hall, A., Wunderer, S., Hoßfeld, T.: Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements. Network. 1, 215–232 (2021).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ Download ]
Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user’s perspective on a large scale. Here, network measurements are performed directly on the end-users’ devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.

@article{info3-article-2021-5, abstract = {Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user’s perspective on a large scale. Here, network measurements are performed directly on the end-users’ devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.}, author = {Wamser, Florian and Seufert, Anika and Hall, Andrew and Wunderer, Stefan and Hoßfeld, Tobias}, journal = {Network}, keywords = {can}, month = 9, number = 2, pages = {215–232}, title = {Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements}, volume = 1, year = 2021 }

%0 Journal Article %1 info3-article-2021-5 %A Wamser, Florian %A Seufert, Anika %A Hall, Andrew %A Wunderer, Stefan %A Hoßfeld, Tobias %D 2021 %J Network %N 2 %P 215--232 %T Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements %V 1 %X Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user’s perspective on a large scale. Here, network measurements are performed directly on the end-users’ devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.
Seufert, A., Wamser, F., Yarish, D., Macdonald, H., Hoßfeld, T.: QoE Models in the Wild: Comparing Video QoE Models Using a Crowdsourced Data Set. 2021 13th International Conference on Quality of Multimedia Experience (QoMEX). , Montreal, Canada (virtual conference) (2021).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ Download ]
Crowdsourced measurements solve the problem of being able to assess the performance of a communication network from an end-user perspective, but the new characteristics of the data pose new challenges for QoE modeling. In contrast to existing laboratory or network measurements, this type of measurement at the end-user device primarily involves taking a large number of short sample measurements, which, however, are rich in measured parameters, including many user-, application-, and device-related parameters. To test the applicability and to facilitate the integration of such data, we applied four QoE models from the literature to 290k worldwide video streaming measurements from a commercial data set from August to October 2020. In this work, we will therefore first describe the crowdsourcing video streaming data set to provide insights into the properties of video streaming KPIs in the real-world. Second, we run four popular QoE models using this data set, compare the resulting QoE scores, and derive the impact of individual KPIs for each model. We show that the models assess the QoE at least differently, but sometimes with contradicting statements. Reading this paper, it becomes evident that more work and subjective studies, based on real-world data like the one we have shown, are needed to extend the current QoE models.

@inproceedings{info3-inproceedings-2021-12, abstract = {Crowdsourced measurements solve the problem of being able to assess the performance of a communication network from an end-user perspective, but the new characteristics of the data pose new challenges for QoE modeling. In contrast to existing laboratory or network measurements, this type of measurement at the end-user device primarily involves taking a large number of short sample measurements, which, however, are rich in measured parameters, including many user-, application-, and device-related parameters. To test the applicability and to facilitate the integration of such data, we applied four QoE models from the literature to 290k worldwide video streaming measurements from a commercial data set from August to October 2020. In this work, we will therefore first describe the crowdsourcing video streaming data set to provide insights into the properties of video streaming KPIs in the real-world. Second, we run four popular QoE models using this data set, compare the resulting QoE scores, and derive the impact of individual KPIs for each model. We show that the models assess the QoE at least differently, but sometimes with contradicting statements. Reading this paper, it becomes evident that more work and subjective studies, based on real-world data like the one we have shown, are needed to extend the current QoE models.}, address = {Montreal, Canada (virtual conference)}, author = {Seufert, Anika and Wamser, Florian and Yarish, David and Macdonald, Hunter and Hoßfeld, Tobias}, booktitle = {2021 13th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {can}, month = 6, title = {QoE Models in the Wild: Comparing Video QoE Models Using a Crowdsourced Data Set}, year = 2021 }

%0 Conference Paper %1 info3-inproceedings-2021-12 %A Seufert, Anika %A Wamser, Florian %A Yarish, David %A Macdonald, Hunter %A Hoßfeld, Tobias %B 2021 13th International Conference on Quality of Multimedia Experience (QoMEX) %C Montreal, Canada (virtual conference) %D 2021 %T QoE Models in the Wild: Comparing Video QoE Models Using a Crowdsourced Data Set %X Crowdsourced measurements solve the problem of being able to assess the performance of a communication network from an end-user perspective, but the new characteristics of the data pose new challenges for QoE modeling. In contrast to existing laboratory or network measurements, this type of measurement at the end-user device primarily involves taking a large number of short sample measurements, which, however, are rich in measured parameters, including many user-, application-, and device-related parameters. To test the applicability and to facilitate the integration of such data, we applied four QoE models from the literature to 290k worldwide video streaming measurements from a commercial data set from August to October 2020. In this work, we will therefore first describe the crowdsourcing video streaming data set to provide insights into the properties of video streaming KPIs in the real-world. Second, we run four popular QoE models using this data set, compare the resulting QoE scores, and derive the impact of individual KPIs for each model. We show that the models assess the QoE at least differently, but sometimes with contradicting statements. Reading this paper, it becomes evident that more work and subjective studies, based on real-world data like the one we have shown, are needed to extend the current QoE models.

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

Teaching

I am currently involved in the following teaching activities: Bachelor Theses, Master Theses, Practical Courses, Seminar. Topics for Bachelor Theses, Master Theses or Practical Courses can be requested at any time. A list of open topics can also be found here: Open Topics.

Anika Seufert M. Sc. (née Schwind)

Research Interests

Recent Publications

Teaching

Data privacy protection

Data privacy protection

Picture credits