Towards Effective Orchestration of AI x DB Workloads

2026-03-04

AI Summary

This work addresses the challenges of traditional AI analytics in multi-tenant heterogeneous data systems, including high computational overhead, limited robustness to data drift, and heightened security risks. To overcome these limitations, the paper proposes a deep integration of AI capabilities into the database engine, co-optimizing query processing and model execution while re-engineering transaction management and access control mechanisms to support efficient end-to-end AI lifecycle operations. The authors design a unified optimization and secure execution framework for AI-native databases (AIxDB), which synergistically combines query optimization, distributed scheduling, heterogeneous hardware adaptation, and fine-grained security enforcement. Preliminary experiments demonstrate the architecture’s effectiveness in mitigating data drift and optimizing resource scheduling, offering a foundational technical pathway toward secure, high-performance AI-native database systems.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Philosophy and Ethics of AI: Privacy & Security
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

AI-driven analytics are increasingly crucial to data-centric decision-making. The practice of exporting data to machine learning runtimes incurs high overhead, limits robustness to data drift, and expands the attack surface, especially in multi-tenant, heterogeneous data systems. Integrating AI directly into database engines, while offering clear benefits, introduces challenges in managing joint query processing and model execution, optimizing end-to-end performance, coordinating execution under resource contention, and enforcing strong security and access-control guarantees. This paper discusses the challenges of joint DB-AI, or AIxDB, data management and query processing within AI-powered data systems. It presents various challenges that need to be addressed carefully, such as query optimization, execution scheduling, and distributed execution over heterogeneous hardware. Database components such as transaction management and access control need to be re-examined to support AI lifecycle management, mitigate data drift, and protect sensitive data from unauthorized AI operations. We present a design and preliminary results to demonstrate what may be key to the performance for serving AIxDB queries.

Problem

• AIxDB
• query processing
• resource contention
• data drift
• access control

Innovation

• AIxDB
• in-database AI
• query optimization
• execution scheduling
• access control