Generating Causal Domain Knowledge for Cloud Systems Monitoring: Unterschied zwischen den Versionen

Aktuelle Version vom 14. März 2023, 18:03 Uhr

Vortragende(r)	Rakan Al Masri
Vortragstyp	Bachelorarbeit
Betreuer(in)	Pawel Bielski
Termin	Fr 17. März 2023
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.

@@ Zeile 2: / Zeile 2: @@
 |vortragender=Rakan Al Masri
 |email=rakanalmasri97@gmail.com
-|vortragstyp=Proposal
+|vortragstyp=Bachelorarbeit
 |betreuer=Pawel Bielski
-|termin=Institutsseminar/2022-12-02 Zusatztermin
+|termin=Institutsseminar/2023-03-17-IPD-Boehm
 |vortragsmodus=in Präsenz
-|kurzfassung=Recently the authors of the DomainML framework for Domain Knowledge Guided Machine Learning showed that heuristically generated hierarchical and textual domain knowledge could improve the performance of machine learning tasks in cloud system monitoring.
+|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, they were unsuccessful in generating useful causal knowledge. The reason might be that the causal knowledge was generated with very simple heuristics.
+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 work, we plan to use causal learning algorithms to generate various forms of causal knowledge. We will then evaluate them on cloud system monitoring machine learning tasks with the DomainML framework.
+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.
 }}