Brown Database Group

Brown University's Database Group is one of the world-leaders in database research. The group has been instrumental in developing influential systems, such as the Aurora and Borealis stream processing engines, the C-Store column-oriented DBMS, the H-Store distributed main-memory OLTP DBMS, and the SciDB array-centric scientific DBMS. Currently, the group's research interests include systems at the intersection of databases and artificial intelligence.

News

Recent Publications

  • VectraFlow: Long-Horizon Semantic Processing over Data and Event Streams with LLMs
    Shu Chen, Junhan Liu, Deepti Raghavan, Uğur Çetintemel
    Preprint 2026
    [Paper]
  • Evergreen: Efficient Claim Verification for Semantic Aggregates
    Alexander W. Lee, Benjamin Han, Shayak Sen, Sam Yeom, Uğur Çetintemel, Anupam Datta
    Preprint 2026
    [Paper] [Code]
  • Continuous Prompts: LLM-Augmented Pipeline Processing over Unstructured Streams
    Shu Chen, Deepti Raghavan, Uğur Çetintemel
    Preprint 2026
    [Paper]
  • Credo: Declarative Control of LLM Pipelines via Beliefs and Policies
    Duo Lu, Andrew Crotty, Uğur Çetintemel
    VLDB (Demo Track) 2026
    [Paper] [Code]
  • Making Prompts First-Class Citizens for Adaptive LLM Pipelines
    Uğur Çetintemel, Shu Chen, Alexander W. Lee, Deepti Raghavan, Duo Lu, Andrew Crotty
    CIDR 2026
    [Paper]
  • Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems
    Alexander W. Lee, Justin Chan, Michael Fu, Nicolas Kim, Akshay Mehta, Deepti Raghavan, Uğur Çetintemel
    VLDB 2025
    [Paper]
  • VectraFlow: Integrating Vectors into Stream Processing
    Duo Lu, Siming Feng, Jonathan Zhou, Franco Solleza, Malte Schwarzkopf, Uğur Çetintemel
    CIDR 2025
    [Paper]
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