05. 10. 2016
Events, NetEye, Real User Experience, Unified Monitoring
17. 08. 2016
Susanne Greiner
Events, NetEye, Real User Experience, Unified Monitoring
Automatic Network Management – A Challenging Goal
Würth Phoenix – Research News
Currently researchers around the world are competing to bring automatic network management to the next level. Würth-Phoenix S.r.l. has recently released a dataset and is currently organizing one of this year’s ECML-PKDD discovery challenges together with The H2020 EU 5G-Cognet project and the University of Trento.
The challenge consists in a multi-class single label classification task of network traffic, as it could be generated by a small company on an average working day. The goal is to predict which application sent which of the requests of the day, while only metrics that do not directly contain this information are available. This challenge is one of the first explorations of ML for automatic network analysis exposed to the public. Our goal is to promote the use of ML for network-related tasks in general and, at the same time, to assess the participants’ ability to quickly build a learning-based system showing a reliable performance. Additionally, one difficulty of using ML for network-related applications is the lack of datasets for training and evaluating different algorithms. Hopefully the provided dataset may help to have reference points in the future and make more advanced research possible.
Many teams around the world have already responded shown their interest in the challenge and are currently giving their best to provide the most successful method for the task.
More detailed information and the download of the dataset can be found here.
Susanne Greiner
Hi there! My name is Susanne and I joined Würth-Phoenix early in 2015. Ever since I can remember computers and the perfection that can be reached by them have been very fascinating for me. I built my first personal PC using components from about 20 broken ones at the age of 11 and fell in love with open source, visualization and data analysis shortly afterwards. I hold a master in experimental physics (University of Erlangen, Germany) and a PhD in computer science (Universtiy of Trento, Italy) my main interests are machine learning, visualization techniques, statistics and optimization. As long as an algorithm of mine runs at night and I get new interesting results the morning after I am able to sleep well. Beside computers I also like music, inline skating, and skiing.
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