Summary of Lemma-rca: a Large Multi-modal Multi-domain Dataset For Root Cause Analysis, by Lecheng Zheng et al.
LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis
by Lecheng Zheng, Zhengzhang Chen, Dongjie Wang, Chengyuan Deng, Reon Matsuoka, Haifeng Chen
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Root cause analysis (RCA) is crucial for enhancing the reliability and performance of complex systems. LEMMA-RCA, a large dataset designed for diverse RCA tasks across multiple domains and modalities, addresses the lack of open-source datasets tailored for RCA. The dataset features real-world fault scenarios from IT and OT operation systems, including microservices, water distribution, and water treatment systems, with hundreds of system entities involved. Eight baseline methods are tested on LEMMA-RCA under various settings, demonstrating its high quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LEMMA-RCA is a new tool to help improve complex systems by finding the original cause of problems. This dataset has many different scenarios from real-life situations like IT and OT operation systems, microservices, water distribution, and more. It’s used to test how well methods work at finding these causes. |
