DB2 advisor: an optimizer smart enough to recommend its own indexes

By Gary Valentin, M. Zuliani, D. Zilio, G. Lohman, Alan Skelley. Proceedings / International Conference on Data Engineering, 2000

360 citations recorded by the source corpus.

How researchers see this paper

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

Database tuning advisor for microsoft SQL server 2005: demo (2005, background): “Indeed, today’s major commercial database systems include as part of their product, automated tools such as [2,3,4,6,15,18,21] that analyze a representative workload consisting of queries and updates that run against the database, and recommend appropriate changes to physical design for the workload”
To Tune or not to Tune? A Lightweight Physical Design Alerter (2006, background): “Several pieces of work (e.g., [2, 5, 7, 13, 15]) present solutions that consider diﬀerent physical structures, and some of these ideas started appearing in commercial products (e.g., [1, 8, 14]).”
Data-centric Artificial Intelligence: A Survey (2023, neutral): “Then, they apply either a greedy algorithm [37] or dynamic programming [221] to select the indexing strategy.”
Integrating vertical and horizontal partitioning into automated physical database design (2004, methodology): “We use TPC-H database [21] in our experiments.”
DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning (2022, background): “, a storage budget), index tuning tools [13, 21, 35] recommend a set of appropriate indexes for improving the performance of the workload.”