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NeurDB: An AI-powered Autonomous Data System

Authors :
Ooi, Beng Chin
Cai, Shaofeng
Chen, Gang
Shen, Yanyan
Tan, Kian-Lee
Wu, Yuncheng
Xiao, Xiaokui
Xing, Naili
Yue, Cong
Zeng, Lingze
Zhang, Meihui
Zhao, Zhanhao
Source :
SCIENCE CHINA Information Sciences 67, 10 (2024)
Publication Year :
2024

Abstract

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.

Details

Database :
arXiv
Journal :
SCIENCE CHINA Information Sciences 67, 10 (2024)
Publication Type :
Report
Accession number :
edsarx.2405.03924
Document Type :
Working Paper
Full Text :
https://doi.org/10.1007/s11432-024-4125-9