mlsanityData to 14 Oct 2026

NeurDB: An AI-powered Autonomous Data System

arXiv 2405.03924 ↗First posted 7 May 2024cs.DB cs.AI cs.LGcited by 5

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Abstract (arXiv)

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 33 of its 76 references that are in the map. First posted on arXiv 7 May 2024.

Builds on

33 of 76 references.

References · 33

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21 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2024MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Trainingcited by 5
  2. 2025In-Context Adaptation to Concept Drift for Learned Database Operationscited by 3
  3. 2025Generative AI for Healthcare: Fundamentals, Challenges, and Perspectivescited by 3
  4. 2025NeurStore: Efficient In-database Deep Learning Model Management Systemcited by 2
  5. 2025Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Designcited by 0