Clustering Techniques for Out-of-Core Multi-resolution Modeling

I
IEEE_VIS
2005
16th IEEE Visualization 2005 (VIS 2005)
Abstract - Clustering Techniques for Out-of-Core Multi-resolution Modeling

	This Article
	Subscribers, please Login Purchase article: $19 PDF IEEE Xplore Subscribers RSS feed
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Emanuele Danovaro Articles by Leila De Floriani Articles by Enrico Puppo Articles by Hanan Samet

16th IEEE Visualization 2005 (VIS 2005)

Minneapolis, Minnesota

October 23-October 28

ISBN: 0-7803-9462-3

	ASCII Text	x
	Emanuele Danovaro, Leila De Floriani, Enrico Puppo, Hanan Samet, "Clustering Techniques for Out-of-Core Multi-resolution Modeling," Visualization Conference, IEEE, pp. 113, 16th IEEE Visualization 2005 (VIS 2005), 2005.

	BibTex	x
	@article{ 10.1109/VIS.2005.15, author = {Emanuele Danovaro and Leila De Floriani and Enrico Puppo and Hanan Samet}, title = {Clustering Techniques for Out-of-Core Multi-resolution Modeling}, journal ={Visualization Conference, IEEE}, volume = {0}, year = {2005}, isbn = {0-7803-9462-3}, pages = {113}, doi = {http://doi.ieeecomputersociety.org/10.1109/VIS.2005.15}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Visualization Conference, IEEE TI - Clustering Techniques for Out-of-Core Multi-resolution Modeling SN - 0-7803-9462-3 SP EP A1 - Emanuele Danovaro, A1 - Leila De Floriani, A1 - Enrico Puppo, A1 - Hanan Samet, PY - 2005 VL - 0 JA - Visualization Conference, IEEE ER -

Emanuele Danovaro, University of Genova

Leila De Floriani, University of Genova

Enrico Puppo, University of Genova

Hanan Samet, University of Maryland at College Park

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/VIS.2005.15

Thanks to improvements in simulation tools, high resolution scanning facilities and multidimensional medical imaging, huge datasets are commonly available. Multi-resolution models manage the complexity of such data sets, by varying resolution and focusing detail in specific areas of interests. Since many currently available data sets cannot fit in main memory, the need arises to design data structures, construction and query algorithms for multi-resolution models which work in secondary memory.

Citation:

Emanuele Danovaro, Leila De Floriani, Enrico Puppo, Hanan Samet, "Clustering Techniques for Out-of-Core Multi-resolution Modeling," ieee_vis, pp.113, 16th IEEE Visualization 2005 (VIS 2005), 2005

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.