Self-Tuning Database Systems: A Decade of Progress

By Surajit Chaudhuri, Vivek R. Narasayya. Very Large Data Bases Conference, 2007

376 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1b2457906994b5942b0ecc6e0ca38e2e3b2450c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Tianheng Cheng
2. Dana Van Aken
3. Ji Zhang
4. Guoliang Li
5. Yu Liu
6. Geoffrey J. Gordon
7. Bohan Zhang
8. Ke Zhou

Citations in the corpus

• An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning (2019, background): “DBMS tuning has been an interesting and active area of research in the last two decades [1, 4, 6, 7, 10, 11, 16, 22, 37, 51, 52, 56, 57].”
• Automatic Database Management System Tuning Through Large-scale Machine Learning (2017, background): “Much of the previous work on automatic database tuning focused on choosing the best logical or physical design of a database [16, 65], such as selecting indexes [28, 17, 59], partitioning schemes [8, 38, 41, 19], or materialized views [7].”
• MINT: Multi-Vector Search Index Tuning (2025, unknown): “We use the term configuration to mean a set of indexes [21].”