Fabian Poignée M. Sc.

Telefon	(0931) 31-85960
E-Mail	fabian.poignee@.uni-wuerzburg.de
Raum	A212
Anschrift	Lehrstuhl für Informatik III Am Hubland D-97074 Würzburg

Contents: Working Group | Projects | Teaching | Publications |

Research Interests

Some of my research interests include the following:

Group-based Communication
Quality of Experience (QoE) of Internet applications
Artificial Intelligence and machine learning for networks
Performance evaluation and modeling of communication systems

2022[ to top ]

Loh, F., Wamser, F., Poignée, F., Geißler, S., Hoßfeld, T.: YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis. Nature, Scientific Data. (2022).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

@article{info3-article-2022-17, abstract = {Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.}, author = {Loh, Frank and Wamser, Florian and Poignée, Fabian and Geißler, Stefan and Hoßfeld, Tobias}, journal = {Nature, Scientific Data}, keywords = {sys:hidden:article}, month = 6, title = {YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis}, year = 2022 }

%0 Journal Article %1 info3-article-2022-17 %A Loh, Frank %A Wamser, Florian %A Poignée, Fabian %A Geißler, Stefan %A Hoßfeld, Tobias %D 2022 %J Nature, Scientific Data %T YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis %X Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.
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 ]
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.
Supervisor: Poignée, F.: Identifying Network Loads in Mobile Messaging Applications, (2022).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In mobile messaging applications, different types of media are exchanged. Depending on the application, compression is used to reduce network load. In this work, a testbed shall be setup to analyze network load for different applications and media types in order to identify where compression is used and to which extent this affects the services.

@mastersthesis{info3-bachelor-2022-3, abstract = {In mobile messaging applications, different types of media are exchanged. Depending on the application, compression is used to reduce network load. In this work, a testbed shall be setup to analyze network load for different applications and media types in order to identify where compression is used and to which extent this affects the services.}, author = {Supervisor: Poignée, Fabian}, keywords = {sys:hidden:supervisor:fabian.poignée}, school = {University of Würzburg}, title = {Identifying Network Loads in Mobile Messaging Applications}, year = 2022 }

%0 Thesis %1 info3-bachelor-2022-3 %A Supervisor: Poignée, Fabian %D 2022 %T Identifying Network Loads in Mobile Messaging Applications %X In mobile messaging applications, different types of media are exchanged. Depending on the application, compression is used to reduce network load. In this work, a testbed shall be setup to analyze network load for different applications and media types in order to identify where compression is used and to which extent this affects the services.
Supervisor: Poignée, F.: Identifying Social Networks within Mobile Messaging, (2022).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In this work, a tool for data extraction from mobile messaging apps shall be implemented. Then, a study shall be conducted and evaluated to identify and model social networks based on the collected data.

@mastersthesis{info3-bachelor-2022-2, abstract = {In this work, a tool for data extraction from mobile messaging apps shall be implemented. Then, a study shall be conducted and evaluated to identify and model social networks based on the collected data.}, author = {Supervisor: Poignée, Fabian}, keywords = {sys:hidden:supervisor:fabian.poignée}, school = {University of Würzburg}, title = {Identifying Social Networks within Mobile Messaging}, year = 2022 }

%0 Thesis %1 info3-bachelor-2022-2 %A Supervisor: Poignée, Fabian %D 2022 %T Identifying Social Networks within Mobile Messaging %X In this work, a tool for data extraction from mobile messaging apps shall be implemented. Then, a study shall be conducted and evaluated to identify and model social networks based on the collected data.

2021[ to top ]

Loh, F., Poignée, F., Wamser, F., Leidinger, F., Hoßfeld, T.: Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Sensors. 21, 4172 (2021).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.

@article{info3-article-2021-3, abstract = {Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.}, author = {Loh, Frank and Poignée, Fabian and Wamser, Florian and Leidinger, Ferdinand and Hoßfeld, Tobias}, journal = {Sensors}, keywords = {can}, number = 12, pages = 4172, title = {Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming}, volume = 21, year = 2021 }

%0 Journal Article %1 info3-article-2021-3 %A Loh, Frank %A Poignée, Fabian %A Wamser, Florian %A Leidinger, Ferdinand %A Hoßfeld, Tobias %D 2021 %J Sensors %N 12 %P 4172 %T Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming %V 21 %X Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.

2020[ to top ]

Poignée, F.: Machine Learning Approach to Predict Video Streaming Behavior at Network Layer, (2020).
- [ BibTeX ]
- [ EndNote ]
@mastersthesis{info3-master-2020-15, author = {Poignée, Fabian}, keywords = {sys:hidden:supervisor:frank.loh}, month = 9, school = {University of Würzburg}, title = {Machine Learning Approach to Predict Video Streaming Behavior at Network Layer}, year = 2020 }

%0 Thesis %1 info3-master-2020-15 %A Poignée, Fabian %D 2020 %T Machine Learning Approach to Predict Video Streaming Behavior at Network Layer

2018[ to top ]

Poignée, F.: Influence of Tension on QoE in Video Streaming, (2018).
- [ BibTeX ]
- [ EndNote ]
@mastersthesis{info3-bachelor-2018-4, author = {Poignée, Fabian}, keywords = {sys:hidden:supervisor:frank.loh}, month = 4, school = {University of Würzburg}, title = {Influence of Tension on QoE in Video Streaming}, year = 2018 }

%0 Thesis %1 info3-bachelor-2018-4 %A Poignée, Fabian %D 2018 %T Influence of Tension on QoE in Video Streaming

Fabian Poignée M. Sc.

Research Interests

Data privacy protection

Data privacy protection

Picture credits