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Self-Improving Data Systems

Research on improving queries and data pipelines with AI, database learning, search, and workload feedback while preserving semantics.

How can data systems use AI, database learning, and workload feedback to improve queries, data pipelines, and system behavior?

Current systems: GenRewrite · DAGSmith. Earlier database-learning work: QuickSel · BlinkML.

Methods and questions

  • large language models
  • workload feedback
  • equivalence checking
  • query and pipeline optimization
  • performance ranking

Projects

2023–present

Self-Improving Data Systems

AI, database learning, and workload feedback for improving queries and data pipelines while preserving semantics and operational constraints.

Publications

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}
}
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}
}