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

• URL: http://arxiv.org/abs/2406.05375v2
• Date: Thu, 26 Sep 2024 22:42:49 GMT
• Title: LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis
• Authors: Lecheng Zheng, Zhengzhang Chen, Dongjie Wang, Chengyuan Deng, Reon Matsuoka, Haifeng Chen,
• Abstract summary: Root cause analysis (RCA) is crucial for enhancing the reliability and performance of complex systems. We introduce LEMMA-RCA, a large dataset designed for diverse RCA tasks across multiple domains and modalities. We evaluate the quality of LEMMA-RCA by testing the performance of eight baseline methods on this dataset.
• Score: 32.816594249593955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

Related papers

• Online Multi-modal Root Cause Analysis [61.94987309148539]
  Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems. We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
  arXiv  Detail & Related papers  (2024-10-13T21:47:36Z)
• What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices [91.71951459594074]
  Long language models (LLMs) with extended context windows have significantly improved tasks such as information extraction, question answering, and complex planning scenarios. Existing methods typically utilize the Self-Instruct framework to generate instruction tuning data for better long context capability improvement. We propose the Multi-agent Interactive Multi-hop Generation framework, incorporating a Quality Verification Agent, a Single-hop Question Generation Agent, a Multiple Question Sampling Strategy, and a Multi-hop Question Merger Agent. Our findings show that our synthetic high-quality long-context instruction data significantly enhances model performance, even surpassing models trained on larger amounts of human
  arXiv  Detail & Related papers  (2024-09-03T13:30:00Z)
• Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering [25.577314828249897]
  We propose a novel dataset, MUSIC-AVQA-R, crafted in two steps: rephrasing questions within the test split of a public dataset (MUSIC-AVQA) and introducing distribution shifts to split questions. Experimental results show that this architecture achieves state-of-the-art performance on MUSIC-AVQA-R, notably obtaining a significant improvement of 9.32%.
  arXiv  Detail & Related papers  (2024-04-18T09:16:02Z)
• AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network [0.5437298646956507]
  A novel adaptive multi-hierarchical attention module (AMAM) is proposed to learn multi-scale features and adaptively aggregate salient features from various feature layers. We first fuse information from adjacent feature layers to enhance the detection of smaller targets, thereby achieving multi-scale feature enhancement. Thirdly, we present a novel adaptive multi-hierarchical attention network (AMANet) by embedding the AMAM between the backbone network and the feature pyramid network.
  arXiv  Detail & Related papers  (2024-01-24T03:56:33Z)
• RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models [46.476439550746136]
  Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently. We present RCAgent, a tool-augmented LLM autonomous agent framework for practical and privacy-aware industrial RCA usage. Running on an internally deployed model rather than GPT families, RCAgent is capable of free-form data collection and comprehensive analysis with tools.
  arXiv  Detail & Related papers  (2023-10-25T03:53:31Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework [51.44863255495668]
  Multimodal reasoning is a critical component in the pursuit of artificial intelligence systems that exhibit human-like intelligence. We present Multi-Modal Reasoning(COCO-MMR) dataset, a novel dataset that encompasses an extensive collection of open-ended questions. We propose innovative techniques, including multi-hop cross-modal attention and sentence-level contrastive learning, to enhance the image and text encoders.
  arXiv  Detail & Related papers  (2023-07-24T08:58:25Z)
• Cross-Modal Fine-Tuning: Align then Refine [83.37294254884446]
  ORCA is a cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. We show that ORCA obtains state-of-the-art results on 3 benchmarks containing over 60 datasets from 12 modalities.
  arXiv  Detail & Related papers  (2023-02-11T16:32:28Z)
• More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification [43.35966675372692]
  We show how to train deep networks and build the network architecture. In particular, we show different fusion strategies as well as how to train deep networks and build the network architecture. Our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2020-08-12T17:45:25Z)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.