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

Version vom 29. November 2022, 19:56 Uhr

Vortragende(r)	Rakan Al Masri
Vortragstyp	Proposal
Betreuer(in)	Pawel Bielski
Termin	Fr 2. Dezember 2022
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. However, they were unsuccessful in generating useful causal knowledge. The reason might be that the causal knowledge was generated with very simple heuristics. 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.

@@ Zeile 6: / Zeile 6: @@
 |termin=Institutsseminar/2022-12-02 Zusatztermin
 |vortragsmodus=in Präsenz
-|kurzfassung=Recently, researchers have shown that domain knowledge improves the performance of machine learning tasks. For example, in healthcare, using hierarchical taxonomies of symptoms improved the performance of risk prediction tasks, especially for rare diseases. Similar ideas proved to work also in other contexts, such as cloud system monitoring.
+|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.
-The authors of the DomainML framework for Domain Knowledge Guided Machine Learning showed that generated hierarchical and textual domain knowledge could improve the performance of machine learning tasks in cloud system monitoring. However, they were unsuccessful in generating useful causal knowledge. The reason might be that the causal knowledge was generated with simple heuristics rather than actual causal learning algorithms.
+However, they were unsuccessful in generating useful causal knowledge. The reason might be that the causal knowledge was generated with very simple heuristics.
-This thesis aims to generate various forms of causal knowledge and evaluate them on cloud system monitoring machine learning tasks within the DomainML framework for Domain Knowledge Guided Machine Learning.
+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.
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