6 papers
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
Jalal Mahmud, Eser Kandogan
Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workf…
Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications
Moin Aminnaseri, Farima Fatahi Bayat, Nikita Bhutani +17
NL2SQL systems aim to address the growing need for natural language interaction with data. However, real-world information rarely maps to a single SQL query because (1) users expre…
Effectiveness of Prompt Optimization in NL2SQL Systems
Sairam Gurajada, Eser Kandogan, Sajjadur Rahman
NL2SQL approaches have greatly benefited from the impressive capabilities of large language models (LLMs). In particular, bootstrapping an NL2SQL system for a specific domain can b…
Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI
Eser Kandogan, Nikita Bhutani, Dan Zhang +3
Large language models (LLMs) have gained significant interest in industry due to their impressive capabilities across a wide range of tasks. However, the widespread adoption of LLM…
Towards Operationalizing Heterogeneous Data Discovery
Jin Wang, Yanlin Feng, Chen Shen +2
Querying and exploring massive collections of data sources, such as data lakes, has been an essential research topic in the database community. Although many efforts have been paid…
MageSQL: Enhancing In-context Learning for Text-to-SQL Applications with Large Language Models
Chen Shen, Jin Wang, Sajjadur Rahman +1
The text-to-SQL problem aims to translate natural language questions into SQL statements to ease the interaction between database systems and end users. Recently, Large Language Mo…