2010 Oct 14 at 14:30
DC 1304
Alkis Polyzotis, University of California, Santa Cruz
Database systems rely heavily on indexes in order to achieve good performance. Selecting the appropriate indexes is a difficult optimization problem, and modern database systems are equipped with automated methods that recommend indexes based on some type of workload analysis. Unfortunately, current methods either require advanced knowledge of the database workload, or force the administrator to relinquish control of which indices are created. This talk will summarize our recent work in semi-automatic index tuning, a novel index recommendation technique that addresses the shortcomings of previous methods. Semi-automatic tuning leverages techniques from online optimization, which allows us to prove strong bounds on the quality of its recommendations. The experimental results show that semi-automatic tuning outperforms previous methods by a large margin, offering index recommendations that achieve close to optimal savings in workload evaluation time. Bio: Neoklis Polyzotis is currently an associate professor at UC Santa Cruz. His research focuses on database systems, and in particular on on-line database tuning, scientific data management, and cloud computing. He is the recipient of an NSF CAREER award in 2004 and of an IBM Faculty Award in 2005 and 2006. He has also received the runner-up for best paper in VLDB 2007 and the best newcomer paper award in PODS 2008. He received his PhD from the University of Wisconsin at Madison in 2003.