7 citations · 14 across the 7 of their papers we have counts for
8 papers
AI Query Compilation for Unified and Optimized Execution
Yeounoh Chung, Helena Caminal, Fatma Ozcan
In this vision paper, we propose a novel architectural paradigm for accelerated AI query execution via a unified compiled execution strategy. By compiling the hybrid AI Query as a…
100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models
Yeounoh Chung, Rushabh Desai, Jian He +9
Several data warehouse and database providers have recently introduced extensions to SQL called AI Queries, enabling users to specify functions and conditions in SQL that are evalu…
Fine-Grained Table Retrieval Through the Lens of Complex Queries
Wojciech Kosiuk, Xingyu Ji, Yeounoh Chung +2
Enabling question answering over tables and databases in natural language has become a key capability in the democratization of insights from tabular data sources. These systems fi…
Multi-Objective Agentic Rewrites for Unstructured Data Processing
Lindsey Linxi Wei, Shreya Shankar, Sepanta Zeighami +3
One year ago, we open-sourced DocETL, a declarative system for LLM-powered data processing that, as of March 2026, has 3.7K GitHub stars and users across domains (e.g., journalism,…
Cortex: Workflow-Aware Resource Pooling and Scheduling for Agentic Serving
Nikos Pagonas, Yeounoh Chung, Kostis Kaffes +1
We introduce Cortex, a prototype workflow-aware serving platform designed for agentic workloads. The core principle of Cortex is stage isolation: it provisions dedicated resource p…
High-Fidelity And Complex Test Data Generation For Google SQL Code Generation Services
Shivasankari Kannan, Yeounoh Chung, Amita Gondi +2
The demand for high-fidelity test data is paramount in industrial settings where access to production data is largely restricted. Traditional data generation methods often fall sho…
Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL
Yeounoh Chung, Gaurav T. Kakkar, Yu Gan +2
Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. In particular, improvements in reasoning abilities and t…
CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL
Mohammadreza Pourreza, Hailong Li, Ruoxi Sun +7
In tackling the challenges of large language model (LLM) performance for Text-to-SQL tasks, we introduce CHASE-SQL, a new framework that employs innovative strategies, using test-t…