Database Meets Artificial Intelligence: A Survey (Extended Abstract)

Xuanhe Zhou; Chengliang Chai; Guoliang Li; Ji Sun

doi:10.1109/ICDE55515.2023.00377

Database Meets Artificial Intelligence: A Survey (Extended Abstract)

Xuanhe Zhou^*, Chengliang Chai, Guoliang Li, Ji Sun

^*Corresponding author for this work

Tsinghua University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can make database more intelligent (AI4DB). It is challenging for empirical database optimization techniques (e.g., configuration tuning, query optimization) to meet the high-performance requirement for large-scale database instances, various applications, diversified users. Learning-based techniques can alleviate this problem by exploring high-quality optimization strategies and reusing the historical data/models. On the other hand, database techniques can optimize AI models (DB4AI). AI is hard to deploy in real applications, because it requires developers to write complex codes and train complicated models. Database techniques can be used to reduce the complexity of using AI models, accelerate AI algorithms and provide AI capability inside databases. Thus, both DB4AI and AI4DB have been extensively studied recently.

Original language	English
Title of host publication	Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
Publisher	IEEE Computer Society
Pages	3901-3902
Number of pages	2
ISBN (Electronic)	9798350322279
DOIs	https://doi.org/10.1109/ICDE55515.2023.00377
Publication status	Published - 2023
Externally published	Yes
Event	39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States Duration: 3 Apr 2023 → 7 Apr 2023

Publication series

Name	Proceedings - International Conference on Data Engineering
Volume	2023-April
ISSN (Print)	1084-4627

Conference

Conference	39th IEEE International Conference on Data Engineering, ICDE 2023
Country/Territory	United States
City	Anaheim
Period	3/04/23 → 7/04/23

Keywords

AI4DB
Artificial-Intelligence
DB4AI
Database

Access to Document

10.1109/ICDE55515.2023.00377

Cite this

Zhou, X., Chai, C., Li, G., & Sun, J. (2023). Database Meets Artificial Intelligence: A Survey (Extended Abstract). In Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023 (pp. 3901-3902). (Proceedings - International Conference on Data Engineering; Vol. 2023-April). IEEE Computer Society. https://doi.org/10.1109/ICDE55515.2023.00377

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title = "Database Meets Artificial Intelligence: A Survey (Extended Abstract)",

abstract = "Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can make database more intelligent (AI4DB). It is challenging for empirical database optimization techniques (e.g., configuration tuning, query optimization) to meet the high-performance requirement for large-scale database instances, various applications, diversified users. Learning-based techniques can alleviate this problem by exploring high-quality optimization strategies and reusing the historical data/models. On the other hand, database techniques can optimize AI models (DB4AI). AI is hard to deploy in real applications, because it requires developers to write complex codes and train complicated models. Database techniques can be used to reduce the complexity of using AI models, accelerate AI algorithms and provide AI capability inside databases. Thus, both DB4AI and AI4DB have been extensively studied recently.",

keywords = "AI4DB, Artificial-Intelligence, DB4AI, Database",

author = "Xuanhe Zhou and Chengliang Chai and Guoliang Li and Ji Sun",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 39th IEEE International Conference on Data Engineering, ICDE 2023 ; Conference date: 03-04-2023 Through 07-04-2023",

year = "2023",

doi = "10.1109/ICDE55515.2023.00377",

language = "English",

series = "Proceedings - International Conference on Data Engineering",

publisher = "IEEE Computer Society",

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booktitle = "Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023",

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}

Zhou, X, Chai, C, Li, G & Sun, J 2023, Database Meets Artificial Intelligence: A Survey (Extended Abstract). in Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023. Proceedings - International Conference on Data Engineering, vol. 2023-April, IEEE Computer Society, pp. 3901-3902, 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, United States, 3/04/23. https://doi.org/10.1109/ICDE55515.2023.00377

Database Meets Artificial Intelligence: A Survey (Extended Abstract). / Zhou, Xuanhe; Chai, Chengliang; Li, Guoliang et al.
Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023. IEEE Computer Society, 2023. p. 3901-3902 (Proceedings - International Conference on Data Engineering; Vol. 2023-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Zhou, Xuanhe

AU - Chai, Chengliang

AU - Li, Guoliang

AU - Sun, Ji

PY - 2023

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AB - Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can make database more intelligent (AI4DB). It is challenging for empirical database optimization techniques (e.g., configuration tuning, query optimization) to meet the high-performance requirement for large-scale database instances, various applications, diversified users. Learning-based techniques can alleviate this problem by exploring high-quality optimization strategies and reusing the historical data/models. On the other hand, database techniques can optimize AI models (DB4AI). AI is hard to deploy in real applications, because it requires developers to write complex codes and train complicated models. Database techniques can be used to reduce the complexity of using AI models, accelerate AI algorithms and provide AI capability inside databases. Thus, both DB4AI and AI4DB have been extensively studied recently.

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Database Meets Artificial Intelligence: A Survey (Extended Abstract)

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