4 citations · 15 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…
A Blueprint Architecture of Compound AI Systems for Enterprise
Eser Kandogan, Sajjadur Rahman, Nikita Bhutani +11
Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases. Towards this goa…
CMDBench: A Benchmark for Coarse-to-fine Multimodal Data Discovery in Compound AI Systems
Yanlin Feng, Sajjadur Rahman, Aaron Feng +2
Compound AI systems (CASs) that employ LLMs as agents to accomplish knowledge-intensive tasks via interactions with tools and data retrievers have garnered significant interest wit…
Machop: an End-to-End Generalized Entity Matching Framework
Jin Wang, Yuliang Li, Wataru Hirota +1
Real-world applications frequently seek to solve a general form of the Entity Matching (EM) problem to find associated entities. Such scenarios include matching jobs to candidates…