Computer Science > Databases
[Submitted on 7 Oct 2023 (v1), last revised 26 Sep 2024 (this version, v3)]
Title:Serving Deep Learning Model in Relational Databases
View PDF HTML (experimental)Abstract:Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently. In this visionary paper, we embark on a comprehensive exploration of representative architectures to address the requirement. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS). The potential relation-centric architecture aims to represent a large-scale tensor computation through relational operators. While each of these architectures demonstrates promise in specific use scenarios, we identify urgent requirements for seamless integration of these architectures and the middle ground in-between these architectures. We delve into the gaps that impede the integration and explore innovative strategies to close them. We present a pathway to establish a novel RDBMS for enabling a broad class of data-intensive DL inference applications.
Submission history
From: Lixi Zhou [view email][v1] Sat, 7 Oct 2023 06:01:35 UTC (2,868 KB)
[v2] Tue, 10 Oct 2023 03:51:58 UTC (2,850 KB)
[v3] Thu, 26 Sep 2024 04:33:35 UTC (1,137 KB)
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