# RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems

arXiv: https://arxiv.org/abs/2503.04252 · HTML: https://mlsanity.com/paper/2503.04252/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 53 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods](https://mlsanity.com/paper/2403.20150/) (2024)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Learned Cardinalities: Estimating Correlated Joins with Deep Learning](https://mlsanity.com/paper/1809.00677/) (2018)
- [Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline](https://mlsanity.com/paper/1912.02379/) (2019)
- [Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation](https://mlsanity.com/paper/2102.05766/) (2021)
- [An End-to-End Learning-based Cost Estimator](https://mlsanity.com/paper/1906.02560/) (2019)
- [Unsupervised Path Representation Learning with Curriculum Negative Sampling](https://mlsanity.com/paper/2106.09373/) (2021)
- [A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data](https://mlsanity.com/paper/2404.14999/) (2024)
- [D-Bot: Database Diagnosis System using Large Language Models](https://mlsanity.com/paper/2312.01454/) (2023)
- [Lero: A Learning-to-Rank Query Optimizer](https://mlsanity.com/paper/2302.06873/) (2023)
- [Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems](https://mlsanity.com/paper/2410.19892/) (2024)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition](https://mlsanity.com/paper/2201.05834/) (2022)
- [Robust Multimodal Failure Detection for Microservice Systems](https://mlsanity.com/paper/2305.18985/) (2023)
- [Multi-Grained Multimodal Interaction Network for Entity Linking](https://mlsanity.com/paper/2307.09721/) (2023)
- [ExplainIt! -- A declarative root-cause analysis engine for time series data (extended version)](https://mlsanity.com/paper/1903.08132/) (2019)

## Built on by (most cited first, 4 of 4)

- [ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting](https://mlsanity.com/paper/2510.09734/) (2025)
- [DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphs](https://mlsanity.com/paper/2508.01136/) (2025)
- [Chirp Localization via Fine-Tuned Transformer Model: A Proof-of-Concept Study](https://mlsanity.com/paper/2503.22713/) (2025)
- [DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents](https://mlsanity.com/paper/2607.22165/) (2026)

## Abstract (arXiv)

With the continued migration of storage to cloud database systems,the impact of slow queries in such systems on services and user experience is increasing. Root-cause diagnosis plays an indispensable role in facilitating slow-query detection and revision. This paper proposes a method capable of both identifying possible root cause types for slow queries and ranking these according to their potential for accelerating slow queries. This enables prioritizing root causes with the highest impact, in turn improving slow-query revision effectiveness. To enable more accurate and detailed diagnoses, we propose the multimodal Ranking for the Root Causes of slow queries (RCRank) framework, which formulates root cause analysis as a multimodal machine learning problem and leverages multimodal information from query statements, execution plans, execution logs, and key performance indicators. To obtain expressive embeddings from its heterogeneous multimodal input, RCRank integrates self-supervised pre-training that enhances cross-modal alignment and task relevance. Next, the framework integrates root-cause-adaptive cross Transformers that enable adaptive fusion of multimodal features with varying characteristics. Finally, the framework offers a unified model that features an impact-aware training objective for identifying and ranking root causes. We report on experiments on real and synthetic datasets, finding that RCRank is capable of consistently outperforming the state-of-the-art methods at root cause identification and ranking according to a range of metrics.