Towards Auto-Generated Data Systems

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

doi:10.14778/3611540.3611635

Towards Auto-Generated Data Systems

Journal Article · 01 August 2023 · Proceedings of the VLDB Endowment

DOI:https://doi.org/10.14778/3611540.3611635· OSTI ID:2420484

Cheung, Alvin ^[1]; Ahmad, Maaz Bin Safeer ^[2]; Haynes, Brandon ^[3]; Kittivorawong, Chanwut ^[4]; Laddad, Shadaj ^[4]; Liu, Xiaoxuan ^[4]; Wang, Chenglong ^[3]; Yan, Cong ^[3]

University of California, Berkeley; OSTI
Adobe Research
Microsoft Research
University of California, Berkeley

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.

Research Organization:: Univ. of Washington, Seattle, WA (United States); Univ. of California, Oakland, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0016260; SC0021982

OSTI ID:: 2420484

Journal Information:: Proceedings of the VLDB Endowment, Journal Name: Proceedings of the VLDB Endowment Journal Issue: 12 Vol. 16; ISSN 2150-8097

Publisher:: Association for Computing Machinery (ACM)

Country of Publication:: United States

Language:: English

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