Dynamic Online Data Clustering for Object -Oriented Databases

133 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000.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.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD