43 citations · 58 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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,…
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…
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…
Finding Patterns in a Knowledge Base using Keywords to Compose Table Answers
Mohan Yang, Bolin Ding, Surajit Chaudhuri +1
We aim to provide table answers to keyword queries against knowledge bases. For queries referring to multiple entities, like "Washington cities population" and "Mel Gibson movies",…