%0 Conference Paper %1 Tory:2004:RVH:1038262.1038794 %A Tory, Melanie %A Moller, Torsten %B Proceedings of the IEEE Symposium on Information Visualization %C Washington, DC, USA %D 2004 %I IEEE Computer Society %K algorithms taxonomy visualization %P 151--158 %R 10.1109/INFOVIS.2004.59 %T Rethinking Visualization: A High-Level Taxonomy %U http://dx.doi.org/10.1109/INFOVIS.2004.59 %X We present a novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the userýs conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relate and overlap, and that may inspire research ideas in hybrid visualization areas. %@ 0-7803-8779-3

@inproceedings{Tory:2004:RVH:1038262.1038794, abstract = {We present a novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the userýs conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relate and overlap, and that may inspire research ideas in hybrid visualization areas.}, acmid = {1038794}, added-at = {2012-09-10T15:45:09.000+0200}, address = {Washington, DC, USA}, author = {Tory, Melanie and Moller, Torsten}, biburl = {https://www.bibsonomy.org/bibtex/26614f34cdbf68544d71151a3d6e21ed0/sac}, booktitle = {Proceedings of the IEEE Symposium on Information Visualization}, description = {Rethinking Visualization}, doi = {10.1109/INFOVIS.2004.59}, interhash = {53e3d081eb13453ff8e8e0a738cd4486}, intrahash = {6614f34cdbf68544d71151a3d6e21ed0}, isbn = {0-7803-8779-3}, keywords = {algorithms taxonomy visualization}, numpages = {8}, pages = {151--158}, publisher = {IEEE Computer Society}, series = {INFOVIS '04}, timestamp = {2012-09-10T15:45:09.000+0200}, title = {Rethinking Visualization: A High-Level Taxonomy}, url = {http://dx.doi.org/10.1109/INFOVIS.2004.59}, year = 2004 }