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Tuning Database Configuration Parameters with iTuned

Summary: Adaptive Sampling identifies high-impact DB configuration parameters and optimal settings. Online production experiments via a cycle-stealing executor incur near-zero overhead and portable across DBMS, validated by diverse workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9850
Venue
VLDB
Year
2009
Pagerank
0.00023616398
Overall Rank
424 | 97.06%
DOI
-

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Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
14 Online Aggregation 1997 SIGMOD 0.0010801504
477 Model-Driven Data Acquisition in Sensor Networks 2004 VLDB 0.00022221803
516 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00021196031
663 Adaptive Self-Tuning Memory in DB2 2006 VLDB 0.00018469455
846 Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering 2002 VLDB 0.00015997985
887 Automatic Virtual Machine Configuration for Database Workloads 2008 SIGMOD 0.00015578896
1,443 Compressing SQL Workloads 2002 SIGMOD 0.00011947004
1,797 Effective Use of Block-Level Sampling in Statistics Estimation 2004 SIGMOD 0.00010523169
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