DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges

2025-07-25

AI Summary

Existing database management system (DBMS) and large language model (LLM) integration approaches lack unified design principles, hindering simultaneous optimization of query efficiency, semantic consistency, and system scalability. Method: We propose a systematic integration framework grounded in design principles, categorizing DBMS+LLM architectures into five canonical patterns. Leveraging architectural taxonomy and analysis of state-of-the-art systems, we identify critical trade-offs—e.g., inference latency versus data freshness—and pinpoint key performance bottlenecks. Contribution: We introduce the first integration understanding framework tailored to industrial use cases—including enterprise analytics, intelligent customer service, and data-driven decision-making. We formally articulate three open challenges: scalability, execution efficiency, and semantic consistency. Furthermore, we provide theoretical foundations and practical guidelines for the co-evolution of traditional data management and language-based reasoning. (149 words)

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Modern enterprises are increasingly driven by the DATA+AI paradigm, in which Database Management Systems (DBMSs) and Large Language Models (LLMs) have become two foundational infrastructures powering a wide range of industrial and business applications, such as enterprise analytics, intelligent customer service, and data-driven decision-making. The efficient integration of DBMSs and LLMs within a unified system offers significant opportunities but also introduces new technical challenges. This paper surveys recent developments in DBMS+LLM integration and identifies key future challenges. Specifically, we categorize five representative architectural patterns based on their core design principles, strengths, and trade-offs. Based on this analysis, we further highlight several critical open challenges. We aim to provide a systematic understanding of the current integration landscape and to outline the unresolved issues that must be addressed to achieve scalable and efficient integration of traditional data management and advanced language reasoning in future intelligent applications.

Problem

• Integrating DBMS and LLMs for industrial applications efficiently
• Identifying challenges in DBMS+LLM unified system integration
• Surveying architectural patterns and future scalable integration issues

Innovation

• Integrates DBMS and LLM for unified systems
• Categorizes five architectural design patterns
• Addresses scalable data and language integration