Dynamic Online Data Clustering for Object -Oriented Databases

In this dissertation, we derive three models to clarify the relationships among the benefits and the penalties of dynamic online clustering, and database system parameters. The shortcomings of the existing implementations are discovered by qualitatively analyzing the resource consumption of each dynamic online clustering component, and then improvements are developed based on the analysis results. At the end, simulations are performed to verify the effectiveness of the models.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Wang, Chao-Ming

Contributors dc:contributor

• Belford, Geneva G.

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI9955677

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81973