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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
9851
Venue
VLDB
Year
2009
Pagerank
0.00023628474
Overall Rank
423 | 97.07%
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 0.0010813443
477 Model-Driven Data Acquisition in Sensor Networks 2004 VLDB 0.00022205608
517 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00021193179
661 Adaptive Self-Tuning Memory in DB2 2006 VLDB 0.00018488168
841 Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering 2002 VLDB 0.00015987128
886 Automatic Virtual Machine Configuration for Database Workloads 2008 SIGMOD 0.00015563003
1,443 Compressing SQL Workloads 2002 SIGMOD 0.00011944621
1,790 Effective Use of Block-Level Sampling in Statistics Estimation 2004 SIGMOD 0.00010529479
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