Abstract:
Database and AI are complementary. AI4DB makes databases smarter. Traditional empirical database optimization methods like cost estimation, join order selection, knob tuning, index and view selection cannot meet the high-performance requirements for large-scale database instances, various applications, and diverse users, especially on the cloud. Learning-based methods can help. DB4AI can optimize AI models. Developers must write complex codes and train complex models to deploy AI in real applications. Database techniques can simplify AI models, accelerate AI algorithms, and provide AI capability in databases. Thus DB4AI and AI4DB have been extensively studied recently. This article reviews AI4DB/DB4AI studies. We review learning-based configuration tuning, optimizer, index/view advisor, and security for AI4DB. DB4AI reviews declarative language, data governance, training acceleration, and inference acceleration. Research challenges and future directions conclude.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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