Nikolas Wehner - Lehrstuhl für Informatik III

Telefon	(0931) 31-88150
Telefax	(0931) 31-86632
E-Mail	nikolas.wehner@uni-wuerzburg.de
Raum	A213a
Anschrift	Lehrstuhl für Informatik III Am Hubland D-97074 Würzburg

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

Research Interests

My research interests include the following topics:

Quality of Experience (QoE) of Internet applications
Measurement and analytics of (encrypted) network traffic
Artificial intelligence and machine learning for networks
Performance analysis and modeling of communication systems

Recent Publications

1.
Schleicher, J., Wehner, N., Hoßfeld, T. & Seufert, M. (Not) The Sum of Its Parts: Relating Individual Video and Browsing Stimuli to Web Session QoE. in 16th International Conference on Quality of Multimedia Experience (QoMEX) (2024).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{info3-inproceedings-2024-3, address = {Karlshamn, Sweden}, author = {Schleicher, Johannes and Wehner, Nikolas and Hoßfeld, Tobias and Seufert, Michael}, booktitle = {16th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {sys:hidden:inproceedings}, month = {06}, title = {(Not) The Sum of Its Parts: Relating Individual Video and Browsing Stimuli to Web Session QoE}, year = 2024 }

%0 Conference Paper %1 info3-inproceedings-2024-3 %A Schleicher, Johannes %A Wehner, Nikolas %A Hoßfeld, Tobias %A Seufert, Michael %B 16th International Conference on Quality of Multimedia Experience (QoMEX) %C Karlshamn, Sweden %D 2024 %T (Not) The Sum of Its Parts: Relating Individual Video and Browsing Stimuli to Web Session QoE
1.
Afifi, H. et al. Machine Learning with Computer Networks: Techniques, Datasets and Models. IEEE Access (2024).
- [ BibTeX ]
- [ EndNote ]
@article{info3-article-2024-2, author = {Afifi, Haitham and Pochaba, Sabrina and Boltres, Andreas and Laniewski, Dominic and Haberer, Janek and Paeleke, Leonard and Poorzare, Reza and Stolpmann, Daniel and Wehner, Nikolas and Redder, Adrian and Samikwa, Eric and Seufert, Michael}, journal = {IEEE Access}, keywords = {sys:hidden:article}, month = {04}, title = {Machine Learning with Computer Networks: Techniques, Datasets and Models}, year = 2024 }

%0 Journal Article %1 info3-article-2024-2 %A Afifi, Haitham %A Pochaba, Sabrina %A Boltres, Andreas %A Laniewski, Dominic %A Haberer, Janek %A Paeleke, Leonard %A Poorzare, Reza %A Stolpmann, Daniel %A Wehner, Nikolas %A Redder, Adrian %A Samikwa, Eric %A Seufert, Michael %D 2024 %J IEEE Access %T Machine Learning with Computer Networks: Techniques, Datasets and Models
1.
Seufert, M. et al. Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale. Transactions on Network and Service Management (TNSM) (2024).
- [ BibTeX ]
- [ EndNote ]
@article{info3-article-2024-1, author = {Seufert, Michael and Dietz, Katharina and Wehner, Nikolas and Geißler, Stefan and Schüler, Joshua and Wolz, Manuel and Hotho, Andreas and Casas, Pedro and Hoßfeld, Tobias and Feldmann, Anja}, journal = {Transactions on Network and Service Management (TNSM)}, keywords = {ucn}, month = {03}, title = {Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale}, year = 2024 }

%0 Journal Article %1 info3-article-2024-1 %A Seufert, Michael %A Dietz, Katharina %A Wehner, Nikolas %A Geißler, Stefan %A Schüler, Joshua %A Wolz, Manuel %A Hotho, Andreas %A Casas, Pedro %A Hoßfeld, Tobias %A Feldmann, Anja %D 2024 %J Transactions on Network and Service Management (TNSM) %T Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale
1.
Dietz, K., Wehner, N., Casas, P., Hoßfeld, T. & Seufert, M. Demystifying User-based Active Learning for Network Monitoring Tasks. in 2nd Workshop on Machine Learning & Networking (MaLeNe) as part of NetSys’23 (2023).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{info3-inproceedings-2023-9, address = {Potsdam, Germany}, author = {Dietz, Katharina and Wehner, Nikolas and Casas, Pedro and Hoßfeld, Tobias and Seufert, Michael}, booktitle = {2nd Workshop on Machine Learning & Networking (MaLeNe) as part of NetSys'23}, keywords = {ucn}, month = {09}, title = {Demystifying User-based Active Learning for Network Monitoring Tasks}, year = 2023 }

%0 Conference Paper %1 info3-inproceedings-2023-9 %A Dietz, Katharina %A Wehner, Nikolas %A Casas, Pedro %A Hoßfeld, Tobias %A Seufert, Michael %B 2nd Workshop on Machine Learning & Networking (MaLeNe) as part of NetSys'23 %C Potsdam, Germany %D 2023 %T Demystifying User-based Active Learning for Network Monitoring Tasks
1.
Wehner, N., Seufert, A., Hoßfeld, T. & Seufert, M. Explainable Data-Driven QoE Modelling using XAI. in 15th International Conference on Quality of Multimedia Experience (QoMEX) (2023).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eXplainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.

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

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

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

Nikolas Wehner M. Sc.

Research Interests

Recent Publications

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