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oai:www.ideals.illinois.edu:2142/81591 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Sharad Mehrotra Chakrabarti, Kaushik 2015-09-25T20:19:23Z 2015-09-25T20:19:23Z 10000-01-01 2001 2001 To handle the huge data volumes and fast response time requirements in decision support applications, we develop an approximate query processing technique based on multidimensional wavelets. Our technique constructs compact synopses (comprising of wavelet coefficients) of the relevant database tables and subsequently answers any SQL query by working exclusively on the compact synopses. Our approach provides more accurate answers and faster response times compared to other approximate query answering techniques. Made available in DSpace on 2015-09-25T20:19:23Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3030416.pdf: 11628273 bytes, checksum: e7e365ab72ca2cb463f15e5308b21115 (MD5) Previous issue date: 2001 Embargo set by: Seth Robbins for item 82872 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 169 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001. http://hdl.handle.net/2142/81591 (MiAaPQ)AAI3030416 eng Computer Science Managing Large Multidimensional Datasets Inside a Database System text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.