走向自动生成数据系统

IF 2.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611635

Alvin Cheung, Maaz Bin Safeer Ahmad, Brandon Haynes, Chanwut Kittivorawong, Shadaj Laddad, Xiaoxuan Liu, Chenglong Wang, Cong Yan

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引用次数: 0

摘要

经过几十年的发展，数据库管理系统(dbms)现在是我们每天与之交互的许多数据应用程序的骨干。然而，随着新的数据类型和硬件的出现，构建和优化新的数据系统仍然像关系数据库的鼎盛时期一样困难。在本文中，我们总结了我们在自动化构建和优化数据系统方面的工作。根据我们自己的经验，我们进一步论证任何自动化技术都必须处理三个方面:用户规范、代码生成和结果验证。最后，我们讨论了一个使用视频数据处理的案例研究，以及未来设计自动生成数据系统的研究机会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Towards Auto-Generated Data Systems

After decades of progress, database management systems (DBMSs) are now the backbones of many data applications that we interact with on a daily basis. Yet, with the emergence of new data types and hardware, building and optimizing new data systems remain as difficult as the heyday of relational databases. In this paper, we summarize our work towards automating the building and optimization of data systems. Drawing from our own experience, we further argue that any automation technique must address three aspects: user specification, code generation, and result validation. We conclude by discussing a case study using videos data processing, along with opportunities for future research towards designing data systems that are automatically generated.

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来源期刊

Proceedings of the Vldb Endowment Computer Science-General Computer Science

CiteScore

7.70

自引率

0.00%

发文量

期刊介绍： The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.