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20182023
most citedRepresentation Learning for Attributed Multiplex Heterogeneous Network

479 citations · 1.9k across the 94 of their papers we have counts for

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14 papers · 1 filter

cs.DB2023★ 29 cited

Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

Dawei Gao, Haibin Wang, Yaliang Li +4

Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective,…

cs.DB2023★ 1 cited

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…

cs.DB2022★ 12 cited

Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing

Chenghao Lyu, Qi Fan, Fei Song +8

Big data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary cons…

cs.DB2022★ 6 cited

Sampling-based Estimation of the Number of Distinct Values in Distributed Environment

Jiajun Li, Zhewei Wei, Bolin Ding +3

In data mining, estimating the number of distinct values (NDV) is a fundamental problem with various applications. Existing methods for estimating NDV can be broadly classified int…

cs.DB2022

Banyan: A Scoped Dataflow Engine for Graph Query Service

Li Su, Xiaoming Qin, Zichao Zhang +6

Graph query services (GQS) are widely used today to interactively answer graph traversal queries on large-scale graph data. Existing graph query engines focus largely on optimizing…

cs.DB2021★ 2 cited

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…

cs.DB2021★ 1 cited

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…

cs.DB2021★ 17 cited

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…

cs.DB2021★ 6 cited

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…

cs.DB2021

FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data

Shuyuan Yan, Bolin Ding, Wei Guo +4

Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecas…

cs.DB2021★ 3 cited

A Pluggable Learned Index Method via Sampling and Gap Insertion

Yaliang Li, Daoyuan Chen, Bolin Ding +2

Database indexes facilitate data retrieval and benefit broad applications in real-world systems. Recently, a new family of index, named learned index, is proposed to learn hidden y…

cs.DB2020★ 16 cited

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…

cs.DB2020

Tempura: A General Cost Based Optimizer Framework for Incremental Data Processing (Extended Version)

Zuozhi Wang, Kai Zeng, Botong Huang +10

Incremental processing is widely-adopted in many applications, ranging from incremental view maintenance, stream computing, to recently emerging progressive data warehouse and inte…

cs.DB2019

A Survey and Experimental Analysis of Distributed Subgraph Matching

Longbin Lai, Zhu Qing, Zhengyi Yang +10

Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view to the pr…