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Publikation

LLMs and Databases: A Synergistic Approach to Data Utilization

Fatma Ozcan; Yeounoh Chung; Yannis Chronis; Lyubomir Ganev; Yawen Wang; Carsten Binnig; Johannes Wehrstein; Gaurav Tarlok Kakkar; Sami Abu-el-haija
In: IEEE Data Engineering Bulletin, Vol. 49, No. 1, Pages 32-44, IEEE, 2025.

Zusammenfassung

Large language models (LLMs) are not merely changing computing; they are igniting a revo- lution across industries, from healthcare to finance. Their prowess in tackling complex problems, demonstrated by breakthroughs in chatbots, translation, and code generation, is undeniable. A notable breakthrough in data management, driven by LLMs, is the advancement of natural language to SQL translation. This technology has fueled substantial progress, making database interactions more accessible and enabling the deployment of numerous real-world applications. Yet, even these powerful models falter with latency-sensitive regression problems, a critical need in database perfor- mance optimization. Inspired by the foundational principles of LLMs, we are developing pre-trained cardinality estimation and foundation database models, bridging this gap and unlocking the next generation of database optimization.

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