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20162026
most citedDatabase Learning: Toward a Database that Becomes Smarter Every Time

50 citations · 80 across the 7 of their papers we have counts for

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8 papers · 1 filter

cs.DB2026

DAGSmith: Dependency-Aware Rewriting for dbt-Style SQL Pipelines

Jie Liu, Lin Ma, Barzan Mozafari

Modern analytics is increasingly organized as recurring SQL pipelines rather than isolated SQL statements. Tools such as dbt, which have gained extreme popularity in recent years,…

cs.DB2026

BEGIN AI TRANSACTION: Semantic Isolation for Durable AI Workflows

Barzan Mozafari

An AI execution can now outlive the environment in which it began. What once fit inside one model call increasingly unfolds across pauses, retries, branches, subagents, and model-s…

cs.DB2024

GenRewrite: Query Rewriting via Large Language Models

Jie Liu, Barzan Mozafari

Query rewriting is an effective technique for refining poorly written queries before they reach the query optimizer. However, manual rewriting is not scalable, as it is prone to er…

cs.DB20204 cited

Joins on Samples: A Theoretical Guide for Practitioners

Dawei Huang, Dong Young Yoon, Seth Pettie +1

Despite decades of research on approximate query processing (AQP), our understanding of sample-based joins has remained limited and, to some extent, even superficial. The common be…

cs.DB2018

QuickSel: Quick Selectivity Learning with Mixture Models

Yongjoo Park, Shucheng Zhong, Barzan Mozafari

Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshe…

cs.DB2018

VerdictDB: Universalizing Approximate Query Processing

Yongjoo Park, Barzan Mozafari, Joseph Sorenson +1

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of…