An optimal on-line algorithm for metrical task system
Journal of the ACM (JACM)
The LRU-K page replacement algorithm for database disk buffering
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Online computation and competitive analysis
Online computation and competitive analysis
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
DB2 Advisor: An Optimizer Smart Enough to Recommend its own Indexes
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Automatic physical design tuning: workload as a sequence
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Efficient use of the query optimizer for automated physical design
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Configuration-parametric query optimization for physical design tuning
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Constrained physical design tuning
Proceedings of the VLDB Endowment
QueryScope: visualizing queries for repeatable database tuning
Proceedings of the VLDB Endowment
A Benchmark for Online Index Selection
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Autonomous Management of Soft Indexes
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
On-Line Index Selection for Shifting Workloads
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Adaptive Physical Design for Curated Archives
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Index interactions in physical design tuning: modeling, analysis, and applications
Proceedings of the VLDB Endowment
Proceedings of the 17th International Database Engineering & Applications Symposium
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To obtain a high level of system performance, a database administrator (DBA) must choose a set of indices that is appropriate for the workload. The system can aid in this challenging task by providing recommendations for the index configuration. We propose a new index recommendation technique, termed semi-automatic tuning, that keeps the DBA "in the loop" by generating recommendations that use feedback about the DBA's preferences. The technique also works online, which avoids the limitations of commercial tools that require the workload to be known in advance. The foundation of our approach is the Work Function Algorithm, which can solve a wide variety of online optimization problems with strong competitive guarantees. We present an experimental analysis that validates the benefits of semi-automatic tuning in a wide variety of conditions.