Scalable temporal anomaly causality discovery in large systems: achieving computational efficiency with binary anomaly flag data
Abstract Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults. Identifying anomaly causes in large systems involves investigating a broader set of monitoring variables across multiple subsystems. However, learning graphical causal models (GCMs) comes with...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
SpringerOpen
2026-05-01
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| Serie: | European Physical Journal C: Particles and Fields |
| Accesso online: | https://doi.org/10.1140/epjc/s10052-026-15611-5 |
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