Towards Effective Orchestration of AI x DB Workloads

• URL: http://arxiv.org/abs/2603.03772v1
• Date: Wed, 04 Mar 2026 06:28:01 GMT
• Title: Towards Effective Orchestration of AI x DB Workloads
• Authors: Naili Xing, Haotian Gao, Zhanhao Zhao, Shaofeng Cai, Zhaojing Luo, Yuncheng Wu, Zhongle Xie, Meihui Zhang, Beng Chin Ooi,
• Abstract summary: This paper discusses the challenges of joint DB-AI, or AIxDB, data management and query processing within AI-powered data systems.<n>It presents various challenges that need to be addressed carefully, such as query optimization, execution scheduling, and distributed execution over heterogeneous hardware.<n>We present a design and preliminary results to demonstrate what may be key to the performance for serving AIxDB queries.
• Score: 20.272049179373163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Relatron: Automating Relational Machine Learning over Relational Databases [50.94254514286021]
  We present a study that unifies RDL and DFS in a shared design space and conducts architecture-centric searches across diverse RDB tasks.<n>Our analysis yields three key findings: (1) RDL does not consistently outperform DFS, with performance being highly task-dependent; (2) no single architecture dominates across tasks, underscoring the need for task-aware model selection; and accuracy is an unreliable guide for choice architecture.
  arXiv  Detail & Related papers  (2026-02-26T02:45:22Z)
• LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment [17.572976426351318]
  Generative AI and Agent technologies are transforming enterprise data management and analytics.<n>Traditional database applications and system deployment are fundamentally impacted by AI-driven tools.<n>Data security and compliance are top priorities for organizations adopting AI technologies.
  arXiv  Detail & Related papers  (2025-11-21T07:16:31Z)
• What's the next frontier for Data-centric AI? Data Savvy Agents [71.76058707995398]
  We argue that data-savvy capabilities should be a top priority in the design of agentic systems.<n>We propose four key capabilities to realize this vision: Proactive data acquisition, Sophisticated data processing, Interactive test data synthesis, and Continual adaptation.
  arXiv  Detail & Related papers  (2025-11-02T17:09:29Z)
• Synthesizing Agentic Data for Web Agents with Progressive Difficulty Enhancement Mechanisms [81.90219895125178]
  Web-based 'deep research' agents aim to solve complex question - answering tasks through long-horizon interactions with online tools.<n>These tasks remain challenging, as the underlying language models are often not optimized for long-horizon reasoning.<n>We introduce a two-pronged data synthesis pipeline that generates question - answer pairs by progressively increasing complexity.
  arXiv  Detail & Related papers  (2025-10-15T06:34:46Z)
• DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision [50.89715397781075]
  Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks.<n>We propose DecEx-RAG, which models RAG as a Markov Decision Process (MDP) incorporating decision-making and execution.<n>We show that DecEx-RAG achieves an average absolute performance improvement of $6.2%$ across six datasets.
  arXiv  Detail & Related papers  (2025-10-07T08:49:22Z)
• Autonomous Data Agents: A New Opportunity for Smart Data [50.02229219403014]
  Report argues that DataAgents represent a paradigm shift toward autonomous data-to-knowledge systems.<n>DataAgents transform complex and unstructured data into coherent and actionable knowledge.<n>We first examine why the convergence of agentic AI and data-to-knowledge systems has emerged as a critical trend.
  arXiv  Detail & Related papers  (2025-09-23T06:46:41Z)
• Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems [8.816332263275305]
  Traditional Data+AI systems rely heavily on human experts to orchestrate system pipelines.<n>Existing Data+AI systems have limited capabilities in semantic understanding, reasoning, and planning.<n>We propose the concept of a 'Data Agent' - a comprehensive architecture designed to orchestrate Data+AI ecosystems.
  arXiv  Detail & Related papers  (2025-07-02T11:04:49Z)
• AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic Analysis [11.419119182421964]
  AnDB is an AI-native database that supports traditional O workloads and AI-driven tasks.<n>AnDB allows users to perform semantic queries using intuitive-like statements without requiring AI expertise.<n>AnDB future-proofs data management infrastructure, empowering users to effectively and efficiently harness the full potential of all kinds of data without starting from scratch.
  arXiv  Detail & Related papers  (2025-02-19T15:15:59Z)
• A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data [0.0]
  Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs)<n>Traditional RAG systems typically use a single-agent architecture to handle query generation, data retrieval, and response synthesis.<n>This paper proposes a multi-agent RAG system to address these limitations.
  arXiv  Detail & Related papers  (2024-12-08T07:18:19Z)
• NeurDB: On the Design and Implementation of an AI-powered Autonomous Database [27.13518136879994]
  This paper introduces NeurDB, an AI-powered autonomous database.<n>NeurDB deepens the fusion of AI and databases with adaptability to data and workload drift.<n> Empirical evaluations demonstrate that NeurDB substantially outperforms existing solutions in managing AI analytics tasks.
  arXiv  Detail & Related papers  (2024-08-06T07:48:51Z)
• Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach [61.74489383629319]
  Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests. Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2023-03-05T12:25:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.