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

arXiv: https://arxiv.org/abs/2406.05375 · HTML: https://mlsanity.com/paper/2406.05375/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [DYNOTEARS: Structure Learning from Time-Series Data](https://mlsanity.com/paper/2002.00498/) (2020)
- [On the Role of Sparsity and DAG Constraints for Learning Linear DAGs](https://mlsanity.com/paper/2006.10201/) (2020)
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://mlsanity.com/paper/2310.16340/) (2023)
- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition](https://mlsanity.com/paper/2206.05871/) (2022)
- [Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Survey](https://mlsanity.com/paper/2105.12378/) (2021)
- [BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection](https://mlsanity.com/paper/2405.09330/) (2024)
- [Constructing Large-Scale Real-World Benchmark Datasets for AIOps](https://mlsanity.com/paper/2208.03938/) (2022)
- [X-lifecycle Learning for Cloud Incident Management using LLMs](https://mlsanity.com/paper/2404.03662/) (2024)
- [Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs](https://mlsanity.com/paper/2404.00640/) (2024)
- [MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis](https://mlsanity.com/paper/2306.02831/) (2023)
- [Multi-modal Causal Structure Learning and Root Cause Analysis](https://mlsanity.com/paper/2402.02357/) (2024)

## Built on by (most cited first, 9 of 9)

- [Online Multi-modal Root Cause Identification in Microservice Systems](https://mlsanity.com/paper/2410.10021/) (2024)
- [Bridging Temporal and Textual Modalities: A Multimodal Framework for Automated Cloud Failure Root Cause Analysis](https://mlsanity.com/paper/2601.04709/) (2026)
- [Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought](https://mlsanity.com/paper/2605.14866/) (2026)
- [What a Pooled Leaderboard Does Not Say: Auditing the Scope and Provenance of Method Comparisons in Microservice RCA Benchmarks](https://mlsanity.com/paper/2606.29159/) (2026)
- [RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection](https://mlsanity.com/paper/2407.09698/) (2024)
- [An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning](https://mlsanity.com/paper/2505.08343/) (2025)
- [CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations](https://mlsanity.com/paper/2604.14691/) (2026)
- [Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring](https://mlsanity.com/paper/2609.27069/) (2026)
- [Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition](https://mlsanity.com/paper/2609.36686/) (2026)

## Abstract (arXiv)

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 Information Technology (IT) and Operational Technology (OT) systems, including microservices, water distribution, and water treatment systems, with hundreds of system entities involved. We evaluate the performance of six baseline methods on LEMMA-RCA across various settings, including offline and online models, as well as single and multi-modal settings. Our study demonstrates the utility of LEMMA-RCA in facilitating fair evaluation and promoting the development of more robust RCA techniques. The dataset is publicly available at https://www.lemmarca.info, with archival metadata and access instructions deposited under DOI: https://doi.org/10.5281/zenodo.20516735.