LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916740720492544 |
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| author | Zheng, Lecheng Chen, Zhengzhang Wang, Dongjie Deng, Chengyuan Matsuoka, Reon Chen, Haifeng |
| author_facet | Zheng, Lecheng Chen, Zhengzhang Wang, Dongjie Deng, Chengyuan Matsuoka, Reon Chen, Haifeng |
| contents | Root cause analysis (RCA) is crucial for enhancing the reliability and performance of complex systems. However, progress in this field has been hindered by the lack of large-scale, open-source datasets tailored for RCA. To bridge this gap, we introduce LEMMA-RCA, a large dataset designed for diverse RCA tasks across multiple domains and modalities. LEMMA-RCA features various real-world fault scenarios from IT and OT operation systems, encompassing microservices, water distribution, and water treatment systems, with hundreds of system entities involved. We evaluate the quality of LEMMA-RCA by testing the performance of eight baseline methods on this dataset under various settings, including offline and online modes as well as single and multiple modalities. Our experimental results demonstrate the high quality of LEMMA-RCA. The dataset is publicly available at https://lemma-rca.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05375 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis Zheng, Lecheng Chen, Zhengzhang Wang, Dongjie Deng, Chengyuan Matsuoka, Reon Chen, Haifeng Artificial Intelligence Machine Learning Root cause analysis (RCA) is crucial for enhancing the reliability and performance of complex systems. However, progress in this field has been hindered by the lack of large-scale, open-source datasets tailored for RCA. To bridge this gap, we introduce LEMMA-RCA, a large dataset designed for diverse RCA tasks across multiple domains and modalities. LEMMA-RCA features various real-world fault scenarios from IT and OT operation systems, encompassing microservices, water distribution, and water treatment systems, with hundreds of system entities involved. We evaluate the quality of LEMMA-RCA by testing the performance of eight baseline methods on this dataset under various settings, including offline and online modes as well as single and multiple modalities. Our experimental results demonstrate the high quality of LEMMA-RCA. The dataset is publicly available at https://lemma-rca.github.io/. |
| title | LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.05375 |