Generating Causal Domain Knowledge for Cloud Systems Monitoring
| Vortragende(r) | Rakan Al Masri | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Pawel Bielski | |
| Termin | Fr 2. Dezember 2022 | |
| Vortragssprache | ||
| 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.
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. 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. | |