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AI + Query Intelligence

AI, large language models, program synthesis, and systems techniques for query rewriting, equivalence, optimization, and learned data-system behavior.

Can a data system discover better ways to express and execute a query?

This program studies how AI and synthesis can help data systems propose, verify, and select useful query transformations. The work treats correctness and measurable system behavior as first-class requirements rather than accepting plausible text as an answer.

Methods and questions

  • large language models
  • program synthesis
  • equivalence checking
  • query optimization
  • performance ranking

Projects

Publications

Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning

Barzan Mozafari, Radu Alexandru Burcuta, Alan Cabrera, Andrei Constantin, Derek Francis, David Grömling, Alekh Jindal, Maciej Konkolowicz, Valentin Marian Spac, Yongjoo Park, Russell Razo Carranzo, Nicholas Richardson, Abhishek Roy, Aayushi Srivastava, Isha Tarte, Brian Westphal, Chi Zhang

SIGMOD Demo 2023 · ACM SIGMOD International Conference on Management of Data — Demonstration

Paper ↗Project ↗
Cite
@inproceedings{8ede012b-09b5-4196-bc20-95033ee64807,
  title = {Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning},
  author = {Barzan Mozafari and Radu Alexandru Burcuta and Alan Cabrera and Andrei Constantin and Derek Francis and David Grömling and Alekh Jindal and Maciej Konkolowicz and Valentin Marian Spac and Yongjoo Park and Russell Razo Carranzo and Nicholas Richardson and Abhishek Roy and Aayushi Srivastava and Isha Tarte and Brian Westphal and Chi Zhang},
  booktitle = {ACM SIGMOD International Conference on Management of Data — Demonstration},
  year = {2023},
  doi = {10.1145/3555041.3589681}
}

SlabCity: Whole-Query Optimization using Program Synthesis

Rui Dong, Jie Liu, Yuxuan Zhu, Cong Yan, Barzan Mozafari, Xinyu Wang

PVLDB 2023 · Proceedings of the VLDB Endowment

Paper ↗Project ↗
Cite
@article{def837ec-1591-44a4-a36d-929442c789f3,
  title = {SlabCity: Whole-Query Optimization using Program Synthesis},
  author = {Rui Dong and Jie Liu and Yuxuan Zhu and Cong Yan and Barzan Mozafari and Xinyu Wang},
  journal = {Proceedings of the VLDB Endowment},
  year = {2023},
  doi = {10.14778/3611479.3611515}
}

QuickSel: Quick Selectivity Learning with Mixture Models

Yongjoo Park, Shucheng Zhong, Barzan Mozafari

SIGMOD 2020 · ACM SIGMOD International Conference on Management of Data

Paper ↗Technical Report ↗Project ↗
Cite
@inproceedings{ea3d8506-1f2f-40ea-8bcb-6a74f72e12f0,
  title = {QuickSel: Quick Selectivity Learning with Mixture Models},
  author = {Yongjoo Park and Shucheng Zhong and Barzan Mozafari},
  booktitle = {ACM SIGMOD International Conference on Management of Data},
  year = {2020},
  doi = {10.1145/3318464.3389727}
}

Database Learning: Toward a Database that Becomes Smarter Every Time

Yongjoo Park, Ahmad Shahab Tajik, Michael J. Cafarella, Barzan Mozafari

SIGMOD 2017 · ACM SIGMOD International Conference on Management of Data

Paper ↗Project ↗
Cite
@inproceedings{3cebf7e2-930f-4c8a-aa17-75a4c32cb001,
  title = {Database Learning: Toward a Database that Becomes Smarter Every Time},
  author = {Yongjoo Park and Ahmad Shahab Tajik and Michael J. Cafarella and Barzan Mozafari},
  booktitle = {ACM SIGMOD International Conference on Management of Data},
  year = {2017},
  doi = {10.1145/3035918.3064013}
}

Neighbor-Sensitive Hashing

Yongjoo Park, Michael J. Cafarella, Barzan Mozafari

PVLDB 2015 · Proceedings of the VLDB Endowment

Paper ↗Code ↗Project ↗
Cite
@article{0397a8bb-85c8-48d3-9e58-c230e57d1e43,
  title = {Neighbor-Sensitive Hashing},
  author = {Yongjoo Park and Michael J. Cafarella and Barzan Mozafari},
  journal = {Proceedings of the VLDB Endowment},
  year = {2015},
  doi = {10.14778/2850583.2850589}
}