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SUMMARY:Semi-Automatic Index Tuning for Database Systems
DTSTART:20100621T141500
DTSTAMP:20260407T210845Z
UID:fc66de6e180d71a32ab12c06ac3c42c6ad848835c658e546d166fbc2
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Neoklis (Alkis) Polyzotis\, University of California\, S
 anta Cruz\nDatabase systems rely heavily on indexes in order to achieve go
 od performance. Selecting the appropriate indexes is a difficult optimizat
 ion problem\, and modern database systems are equipped with automated meth
 ods that recommend indexes based on some type of workload analysis. Unfort
 unately\, current methods either require advanced knowledge of the databas
 e workload\, or force the administrator to relinquish control of which ind
 ices are created. This talk will summarize our recent work in semi-automat
 ic index tuning\, a novel index recommendation technique that addresses th
 e shortcomings of previous methods. Semi-automatic tuning leverages techni
 ques from online optimization\, which allows us to prove strong bounds on 
 the quality of its recommendations. The experimental results show that sem
 i-automatic tuning outperforms previous methods by a large margin\, offeri
 ng index recommendations that achieve close to optimal savings in workload
  evaluation time. \nProf. Polyzotis' homepage
LOCATION:BC 01 https://plan.epfl.ch/?room==BC%2001
STATUS:CONFIRMED
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