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LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis

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arxiv 2406.05375 v3 pith:V5NMVTR5 submitted 2024-06-08 cs.AI cs.LG

classification cs.AIcs.LG
keywords lemma-rcadatasetsystemsanalysiscauselargemodalitiesmultiple
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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/.

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Cited by 1 Pith paper

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  1. An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning

    cs.AI 2025-05 reject novelty 6.0 of 10

    MiCCD learns a causal model of abnormal system data and solves for the lowest-cost intervention that would have prevented the anomaly, outperforming six baselines in experiments.

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