43 citations · 124 across the 21 of their papers we have counts for
21 papers
AgentJet: A Distributed Swarm Training Framework for Agentic Reinforcement Learning
Qingxu Fu, Boyin Liu, Shuchang Tao +5
Training reinforcement learning (RL) policies for large language model (LLM) agents requires optimizing multi-turn trajectories that interact with external environments. Existing t…
QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics
Tianjing Zeng, Yuntao Hong, Zhongjun Ding +17
Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineering, it operates in an open, ambig…
SEMA-SQL: Beyond Traditional Relational Querying with Large Language Models
Yin Lin, Tianjing Zeng, Zhongjun Ding +4
Relational databases excel at structured data analysis, but real-world queries increasingly require capabilities beyond standard SQL, such as semantically matching entities across…
Learned Query Optimizer in Alibaba MaxCompute: Challenges, Analysis, and Solutions
Lianggui Weng, Dandan Liu, Wenzhuang Zhu +5
Existing learned query optimizers remain ill-suited to modern distributed, multi-tenant data warehouses due to idealized modeling assumptions and design choices. Using Alibaba's Ma…
BridgeScope: A Universal Toolkit for Bridging Large Language Models and Databases
Lianggui Weng, Dandan Liu, Rong Zhu +2
As large language models (LLMs) demonstrate increasingly powerful reasoning and orchestration capabilities, LLM-based agents are rapidly proliferating for complex data-related task…
AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
Zhongjun Ding, Yin Lin, Tianjing Zeng +3
Text-to-SQL systems translate natural language questions into SQL queries, providing substantial value for non-expert users. While large language models (LLMs) show promising resul…
Delta: A Learned Mixed Cost-based Query Optimization Framework
Jiazhen Peng, Zheng Qu, Xiaoye Miao +1
Query optimizer is a crucial module for database management systems. Existing optimizers exhibit two flawed paradigms: (1) cost-based optimizers use dynamic programming with cost m…
PRICE: A Pretrained Model for Cross-Database Cardinality Estimation
Tianjing Zeng, Junwei Lan, Jiahong Ma +7
Cardinality estimation (CardEst) is essential for optimizing query execution plans. Recent ML-based CardEst methods achieve high accuracy but face deployment challenges due to high…
UniDM: A Unified Framework for Data Manipulation with Large Language Models
Yichen Qian, Yongyi He, Rong Zhu +8
Designing effective data manipulation methods is a long standing problem in data lakes. Traditional methods, which rely on rules or machine learning models, require extensive human…
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)
Pengfei Li, Wenqing Wei, Rong Zhu +3
For efficient query processing, DBMS query optimizers have for decades relied on delicate cardinality estimation methods. In this work, we propose an Attention-based LEarned Cardin…
DILI: A Distribution-Driven Learned Index (Extended version)
Pengfei Li, Hua Lu, Rong Zhu +3
Targeting in-memory one-dimensional search keys, we propose a novel DIstribution-driven Learned Index tree (DILI), where a concise and computation-efficient linear regression model…
Lero: A Learning-to-Rank Query Optimizer
Rong Zhu, Wei Chen, Bolin Ding +4
A recent line of works apply machine learning techniques to assist or rebuild cost-based query optimizers in DBMS. While exhibiting superiority in some benchmarks, their deficienci…
Baihe: SysML Framework for AI-driven Databases
Andreas Pfadler, Rong Zhu, Wei Chen +4
We present Baihe, a SysML Framework for AI-driven Databases. Using Baihe, an existing relational database system may be retrofitted to use learned components for query optimization…
Glue: Adaptively Merging Single Table Cardinality to Estimate Join Query Size
Rong Zhu, Tianjing Zeng, Andreas Pfadler +3
Cardinality estimation (CardEst), a central component of the query optimizer, plays a significant role in generating high-quality query plans in DBMS. The CardEst problem has been…
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation
Yuxing Han, Ziniu Wu, Peizhi Wu +11
Cardinality estimation (CardEst) plays a significant role in generating high-quality query plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced…
Automated Graph Learning via Population Based Self-Tuning GCN
Ronghang Zhu, Zhiqiang Tao, Yaliang Li +1
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of t…
A Unified Transferable Model for ML-Enhanced DBMS
Ziniu Wu, Pei Yu, Peilun Yang +6
Recently, the database management system (DBMS) community has witnessed the power of machine learning (ML) solutions for DBMS tasks. Despite their promising performance, these exis…
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications
Rong Zhu, Andreas Pfadler, Ziniu Wu +6
Structure Learning for Bayesian network (BN) is an important problem with extensive research. It plays central roles in a wide variety of applications in Alibaba Group. However, ex…
FSPN: A New Class of Probabilistic Graphical Model
Ziniu Wu, Rong Zhu, Andreas Pfadler +5
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs). FSPNs are designed to overcome the drawbacks of existing PGMs in te…
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation
Rong Zhu, Ziniu Wu, Yuxing Han +5
Query optimizers rely on accurate cardinality estimation (CardEst) to produce good execution plans. The core problem of CardEst is how to model the rich joint distribution of attri…
AliGraph: A Comprehensive Graph Neural Network Platform
Rong Zhu, Kun Zhao, Hongxia Yang +5
An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements. Graph…