Autonomous Database Partitioning Using Data Mining for High Performance Computing

This material is based upon work supported by the National Science Foundation under Grant No. 0954310. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

Publications

In order by date

Liangzhe Li and Le Gruenwald, "An SLA and Operation Cost Aware Performance Re-Tuning Algorithm for Cloud Databases," technical report, August 2015.

Liangzhe Li and Le Gruenwald, "SMOPD-C: An Autonomous Vertical Partitioning Technique for Distributed Databases on Cluster Computers," The 15th IEEE International Conference on Information Reuse and Integration, IRI '14, San Francisco, 2014.

Liangzhe Li, "Database Vertical Partitioning on Both Single Computers and Cluster Computers," Master's thesis, School of Computer Science, University of Oklahoma, December 2013.

Liangzhe Li and Le Gruenwald, "Self-Managing Online Partitioner for Databases(SMOPD) - A Vertical Database Partitioning System with a Fully Automatic Online Approach.", IDEAS, 2013.

Liangzhe Li and Le Gruenwald, "Autonomous Database Partitioning using Data Mining on Single Computers and Cluster Computers.", IDEAS, 2012.

Le Gruenwald, Sylvain Guinepain and Zhenbo Xing, "AutoClust: Autonomous Database Partitioning using Data Mining for Relations with One-to-One and One-to-Many Relationships", Technical Report, July, 2010.