17 citations · 34 across the 19 of their papers we have counts for
22 papers
ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models
Xinmei Huang, Jie Song, Peng Li +10
Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines. Existing S…
Can Large Language Models be a Cardinality Estimator? An Empirical study
Liangzu Liu, Yiyan Wang, Yinjun Wu +8
Cardinality estimation (CardEst) still remains a challenging problem for DBMS. Recent years have witnessed the success of ML-based cardinality estimators in outperforming tradition…
DeepPrep: An LLM-Powered Agentic System for Autonomous Data Preparation
Meihao Fan, Ju Fan, Yuxin Zhang +7
Data preparation, which aims to transform heterogeneous and noisy raw tables into analysis-ready data, remains a major bottleneck in data science. Recent approaches leverage large…
KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction
Lei Ma, Jinyang Liu, Tieying Zhang +5
Log anomaly detection is crucial for uncovering system failures and security risks. Although logs originate from nested component executions with clear boundaries, this structure i…
ZeroCard: Cardinality Estimation with Zero Dependence on Target Databases -- No Data, No Query, No Retraining
Xianghong Xu, Rong Kang, Xiao He +3
Cardinality estimation is a fundamental task in database systems and plays a critical role in query optimization. Despite significant advances in learning-based cardinality estimat…
ErrorPrism: Reconstructing Error Propagation Paths in Cloud Service Systems
Junsong Pu, Yichen Li, Zhuangbin Chen +6
Reliability management in cloud service systems is challenging due to the cascading effect of failures. Error wrapping, a practice prevalent in modern microservice development, enr…
LogPilot: Intent-aware and Scalable Alert Diagnosis for Large-scale Online Service Systems
Zhihan Jiang, Jinyang Liu, Yichen Li +7
Effective alert diagnosis is essential for ensuring the reliability of large-scale online service systems. However, on-call engineers are often burdened with manually inspecting ma…
Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling
Jiadong Chen, Xiao He, Hengyu Ye +4
In the swiftly evolving domain of cloud computing, the advent of serverless systems underscores the crucial need for predictive auto-scaling systems. This necessity arises to ensur…
Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services
Jiadong Chen, Hengyu Ye, Fuxin Jiang +4
Workload forecasting is pivotal in cloud service applications, such as auto-scaling and scheduling, with profound implications for operational efficiency. Although Transformer-base…
Data-Agnostic Cardinality Learning from Imperfect Workloads
Peizhi Wu, Rong Kang, Tieying Zhang +3
Cardinality estimation (CardEst) is a critical aspect of query optimization. Traditionally, it leverages statistics built directly over the data. However, organizational policies (…
ABase: the Multi-Tenant NoSQL Serverless Database for Diverse and Dynamic Workloads in Large-scale Cloud Environments
Rong Kang, Yanbin Chen, Ye Liu +8
Multi-tenant architectures enhance the elasticity and resource utilization of NoSQL databases by allowing multiple tenants to co-locate and share resources. However, in large-scale…
Adaptive and Efficient Log Parsing as a Cloud Service
Zeyan Li, Jie Song, Tieying Zhang +6
Logs are a critical data source for cloud systems, enabling advanced features like monitoring, alerting, and root cause analysis. However, the massive scale and diverse formats of…
TickIt: Leveraging Large Language Models for Automated Ticket Escalation
Fengrui Liu, Xiao He, Tieying Zhang +6
In large-scale cloud service systems, support tickets serve as a critical mechanism for resolving customer issues and maintaining service quality. However, traditional manual ticke…
PLM4NDV: Minimizing Data Access for Number of Distinct Values Estimation with Pre-trained Language Models
Xianghong Xu, Xiao He, Tieying Zhang +3
Number of Distinct Values (NDV) estimation of a multiset/column is a basis for many data management tasks, especially within databases. Despite decades of research, most existing m…
AdaNDV: Adaptive Number of Distinct Value Estimation via Learning to Select and Fuse Estimators
Xianghong Xu, Tieying Zhang, Xiao He +8
Estimating the Number of Distinct Values (NDV) is fundamental for numerous data management tasks, especially within database applications. However, most existing works primarily fo…
VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era
Rong Kang, Shuai Wang, Tieying Zhang +6
Virtual index, also known as hypothetical indexes, play a crucial role in database query optimization. However, with the rapid advancement of cloud computing and AI-driven models f…
LLMIA: An Out-of-the-Box Index Advisor via In-Context Learning with LLMs
Xinxin Zhao, Xinmei Huang, Haoyang Li +7
Index recommendation is crucial for optimizing database performance. However, existing heuristic- and learning-based methods often rely on inefficient exhaustive search and estimat…
OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
Haoyang Li, Shang Wu, Xiaokang Zhang +9
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements…
Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis
Changhua Pei, Zexin Wang, Fengrui Liu +9
In the realm of microservices architecture, the occurrence of frequent incidents necessitates the employment of Root Cause Analysis (RCA) for swift issue resolution. It is common t…
Query Performance Explanation through Large Language Model for HTAP Systems
Haibo Xiu, Li Zhang, Tieying Zhang +2
In hybrid transactional and analytical processing (HTAP) systems, users often struggle to understand why query plans from one engine (OLAP or OLTP) perform significantly slower tha…
ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
Zhe Xie, Zeyan Li, Xiao He +6
Understanding time series is crucial for its application in real-world scenarios. Recently, large language models (LLMs) have been increasingly applied to time series tasks, levera…
ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting
Hengyu Ye, Jiadong Chen, Shijin Gong +4
The intricate nature of time series data analysis benefits greatly from the distinct advantages offered by time and frequency domain representations. While the time domain is super…