Serving Deep Learning Model in Relational Databases
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910620480176128 |
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| author | Zhou, Lixi Lin, Qi Chowdhury, Kanchan Masood, Saif Eichenberger, Alexandre Min, Hong Sim, Alexander Wang, Jie Wang, Yida Wu, Kesheng Yuan, Binhang Zou, Jia |
| author_facet | Zhou, Lixi Lin, Qi Chowdhury, Kanchan Masood, Saif Eichenberger, Alexandre Min, Hong Sim, Alexander Wang, Jie Wang, Yida Wu, Kesheng Yuan, Binhang Zou, Jia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_04696 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Serving Deep Learning Model in Relational Databases Zhou, Lixi Lin, Qi Chowdhury, Kanchan Masood, Saif Eichenberger, Alexandre Min, Hong Sim, Alexander Wang, Jie Wang, Yida Wu, Kesheng Yuan, Binhang Zou, Jia Databases Artificial Intelligence 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. |
| title | Serving Deep Learning Model in Relational Databases |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2310.04696 |