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oai:www.ideals.illinois.edu:2142/81973 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Belford, Geneva G. Wang, Chao-Ming 2015-09-25T20:21:15Z 2015-09-25T20:21:15Z 10000-01-01 2000 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. Made available in DSpace on 2015-09-25T20:21:15Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9955677.pdf: 5739824 bytes, checksum: 973498ac1cf114f6b6200881a90c3eef (MD5) Previous issue date: 2000 Embargo set by: Seth Robbins for item 83254 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 133 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000. http://hdl.handle.net/2142/81973 (MiAaPQ)AAI9955677 eng Computer Science Dynamic Online Data Clustering for Object -Oriented Databases text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.