Towards Auto-Generated Data Systems

被引:0
|
作者
Cheung, Alvin [1 ]
Ahmad, Maaz Bin Safeer [2 ]
Haynes, Brandon [3 ]
Kittivorawong, Chanwut [1 ]
Laddad, Shadaj [1 ]
Liu, Xiaoxuan [1 ]
Wang, Chenglong [3 ]
Yan, Cong [3 ]
机构
[1] Univ Calif Berkeley, Berkeley, CA 94720 USA
[2] Adobe Res, San Francisco, CA 94107 USA
[3] Microsoft Res, Redmond, WA 98052 USA
来源
PROCEEDINGS OF THE VLDB ENDOWMENT | 2023年 / 16卷 / 12期
基金
美国国家科学基金会;
关键词
D O I
10.14778/3611540.3611635
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:4116 / 4129
页数:14
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