Quelltext der Seite Generating Causal Domain Knowledge for Cloud Systems Monitoring

{{Vortrag
|vortragender=Rakan Al Masri
|email=rakanalmasri97@gmail.com
|vortragstyp=Bachelorarbeit
|betreuer=Pawel Bielski
|termin=Institutsseminar/2023-03-17-IPD-Boehm
|vortragsmodus=in Präsenz
|kurzfassung=While standard machine learning approaches rely solely on data to learn relevant patterns, in certain fields, this may not be sufficient. Researchers in the Healthcare domain, have successfully applied causal domain knowledge to improve prediction quality of machine learning models, especially for rare diseases. The causal domain knowledge informs the machine learning model about similar diseases, thus improving the quality of the predictions.

However, some domains, such as Cloud Systems Monitoring, lack readily
available causal domain knowledge, and thus the knowledge must be approximated.
Therefore, it is important to have a systematic investigation of the processes and
design decision that affect the knowledge generation process.

In this study, we showed how causal discovery algorithms can be employed to generate causal domain knowledge
from raw textual logs in the Cloud Systems Monitoring domain. We also
investigated the impact of various design choices on the domain knowledge
generation process through systematic testing across multiple datasets and
shared the insights we gained. To our knowledge, this is the first time such an
investigation has been conducted.
}}