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20232026
most citedSEED: Domain-Specific Data Curation With Large Language Models

8 citations · 25 across the 16 of their papers we have counts for

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5 papers · 1 filter

cs.DB2026

Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries

Matthew Russo, Yash Agarwal, Tianyu Li +5

Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents. However, the latter operates as an opa…

cs.DB2026

SAGE: Selective Attention-Guided Extraction for Token-Efficient Document Indexing

Xinzhi Wang, Peter Baile Chen, Gerardo Vitagliano +5

Large language models with long context windows can answer complex questions directly from full-length academic, technical, and policy documents, but passing entire documents is of…

cs.DB2025★ 2 cited

Abacus: A Cost-Based Optimizer for Semantic Operator Systems

Matthew Russo, Chunwei Liu, Sivaprasad Sudhir +4

LLMs enable an exciting new class of data processing applications over large collections of unstructured documents. Several new programming frameworks have enabled developers to bu…

cs.DB2025★ 3 cited

The Cambridge Report on Database Research

Anastasia Ailamaki, Samuel Madden, Daniel Abadi +43

On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challe…

cs.DB2023★ 8 cited

SEED: Domain-Specific Data Curation With Large Language Models

Zui Chen, Lei Cao, Sam Madden +7

Data curation tasks that prepare data for analytics are critical for turning data into actionable insights. However, due to the diverse requirements of applications in different do…