On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929439314542592 |
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| author | Zan, Lei Assaad, Charles K. Devijver, Emilie Gaussier, Eric Aït-Bachir, Ali |
| author_facet | Zan, Lei Assaad, Charles K. Devijver, Emilie Gaussier, Eric Aït-Bachir, Ali |
| contents | This paper introduces a new structural causal model tailored for representing threshold-based IT systems and presents a new algorithm designed to rapidly detect root causes of anomalies in such systems. When root causes are not causally related, the method is proven to be correct; while an extension is proposed based on the intervention of an agent to relax this assumption. Our algorithm and its agent-based extension leverage causal discovery from offline data and engage in subgraph traversal when encountering new anomalies in online data. Our extensive experiments demonstrate the superior performance of our methods, even when applied to data generated from alternative structural causal models or real IT monitoring data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06500 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Fly Detection of Root Causes from Observed Data with Application to IT Systems Zan, Lei Assaad, Charles K. Devijver, Emilie Gaussier, Eric Aït-Bachir, Ali Artificial Intelligence This paper introduces a new structural causal model tailored for representing threshold-based IT systems and presents a new algorithm designed to rapidly detect root causes of anomalies in such systems. When root causes are not causally related, the method is proven to be correct; while an extension is proposed based on the intervention of an agent to relax this assumption. Our algorithm and its agent-based extension leverage causal discovery from offline data and engage in subgraph traversal when encountering new anomalies in online data. Our extensive experiments demonstrate the superior performance of our methods, even when applied to data generated from alternative structural causal models or real IT monitoring data. |
| title | On the Fly Detection of Root Causes from Observed Data with Application to IT Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2402.06500 |