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Han, Jiawei Chen, Chen 2015-09-25T20:20:43Z 2015-09-25T20:20:43Z 10000-01-01 2009 2009 In conclusion, the multi-dimensional analysis framework could lead to intuitive and insightful knowledge discovery on graphs, especially when the data is large and complex. Given the emerging trend of huge information networks as listed above, it is an important research topic to devote more efforts to. We point out a few possible future works, especially discovery-driven Graph OLAP. We believe that this is an interesting direction to go, and give our initial thoughts on it. Made available in DSpace on 2015-09-25T20:20:43Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3391903.pdf: 1358113 bytes, checksum: 49a13671f1affc44839a043a4721a883 (MD5) Previous issue date: 2009 Embargo set by: Seth Robbins for item 83138 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 87 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. http://hdl.handle.net/2142/81857 (MiAaPQ)AAI3391903 eng Computer Science Multi-Dimensional Analysis of Graph Data text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.