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Issue No.12 - Dec. (2012 vol.18)
pp: 2603-2612
Eamonn Maguire , University of Oxford
Philippe Rocca-Serra , University of Oxford
Susanna-Assunta Sansone , University of Oxford
Jim Davies , University of Oxford
Min Chen , University of Oxford
Glyph-based visualization can offer elegant and concise presentation of multivariate information while enhancing speed and ease in visual search experienced by users. As with icon designs, glyphs are usually created based on the designers’ experience and intuition, often in a spontaneous manner. Such a process does not scale well with the requirements of applications where a large number of concepts are to be encoded using glyphs. To alleviate such limitations, we propose a new systematic process for glyph design by exploring the parallel between the hierarchy of concept categorization and the ordering of discriminative capacity of visual channels. We examine the feasibility of this approach in an application where there is a pressing need for an efficient and effective means to visualize workflows of biological experiments. By processing thousands of workflow records in a public archive of biological experiments, we demonstrate that a cost-effective glyph design can be obtained by following a process of formulating a taxonomy with the aid of computation, identifying visual channels hierarchically, and defining application-specific abstraction and metaphors.
Data visualization, Glyph design, bioinformatics visualization, Glyph-based techniques, taxonomies, design methodologies
Eamonn Maguire, Philippe Rocca-Serra, Susanna-Assunta Sansone, Jim Davies, Min Chen, "Taxonomy-Based Glyph Design—with a Case Study on Visualizing Workflows of Biological Experiments", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 12, pp. 2603-2612, Dec. 2012, doi:10.1109/TVCG.2012.271
[1] Arrayexpress.
[2] Gene expression omnibus (geo).
[3] Graphviz.
[4] R. Abdullah and R. Hümber., Pictograms, Icons & Signs: A guide to Information Graphics. Thames & Hudson, 2006.
[5] M. Bar, Visual objects in context Nature reviews. Neuroscience, 5(8): 617-629, 2004.
[6] P. Barker and J. Fraser, Sign design guide: a guide to inclusive signage JMU and Sign Design Society, 2000.
[7] J. Bertin., Semiology of graphics: diagrams, networks, maps. University of Wisconsin press, 1983.
[8] R. Borgo,A. Abdul-Rahman, F. Mohamed, P. W. Grant, I. Reppa, L. Floridi,, and M. Chen., An empirical study on using visual embellishments in visualization. to appear in IEEE Transactions on Visualization and Computer Graphics, 2012.
[9] R. Bourqui and M. Westenberg, Visualizing temporal dynamics at the genomic and metabolic level Proc. 13th Int. Conf Information Visualisation, pages 317-322, 2009.
[10] B. Burns,B. E. Shepp, D. McDonough, and W. K, Wiener-Ehrlich. The relation between stimulus analyzability and perceived dimensional structure The Psychology of Learning and Motivation, pages 77-115, 1978.
[11] Y. Cai,M. L. Wilson,, and J. Peccoud., Genocad for igem: a grammatical approach to the design of standard-compliant constructs. Nucleic Acids Res, 38(8): 2637-44, May 2010.
[12] M. Chen and H. Jänicke., An information-theoretic framework for visualization. IEEE Transactions on Visualization and Computer Graphics, 16(6): 1206-1215, 2010.
[13] M. Chuah and S. Eick, Information rich glyphs for software management data IEEE Computer Graphics and Applications, 18: 24-29, 1998.
[14] T. Czauderna, C. Klukas, and F. Schreiber, Editing, validating and trans-lating of sbgn maps Bioinformatics; 26(18): 2340-2341, 2010.
[15] J. Duncan and G. Humphreys, Visual search and stimulus similarity Psychological review, 96(3): 433-491, 1989.
[16] J. Franks and J. Bransford, Abstraction of visual patterns Journal of experimental psychology, 90(1): 65-139, 1971.
[17] M. Green, Toward a perceptual science of multidimensional data visualization: Bertin and beyond ERGO/GERO Human Factors Science, 1998.
[18] S. Handelt and S. Imai, The free classification of analyzable and unana-lyzable stimuli Attention, Perception, & Psychophysics, 12(1): 108-224, 1972.
[19] C. G. Healey and J. T. Enns., Attention and visual memory attention and visual memory in visualization and computer graphics IEEE Transactions on Visualization and Computer Graphics, 18(7): 1170-1188, 2011.
[20] J. Heer,S. K. Card,, and J. A. Landay., prefuse: A toolkit for interactive information visualization. In Proc. ACM CHI, pages 421-430, 2005.
[21] K. Hemenway., Psychological issues in the use of icons in command menus. In Proc. ACM CHI, pages 20-23, 1982.
[22] B. H. Junker, C. Klukas, and F. Schreiber., Vanted: A system for advanced data analysis and visualization in the context of biological networks BMC Bioinformatics, 2006.
[23] A. Karve and M. Gleicher., Glyph-based overviews of large datasets in structural bioinformatics. In Proc. 11th Int. Conf Information Visualization, Supplements, pages 1-6, 2007.
[24] R. Kinchla and J. Wolfe, The order of visual processing: “top-down,” “bottom-up”, or “middle-out” Perception & Psychophysics 25(3), 1979.
[25] G. Kindlmann and C.-F. Westin, Diffusion tensor visualization with glyph packing IEEE Transactions on Visualization and Computer Graphics, 12(5): 1329-1335, 2006.
[26] D. Krantz and A. Tversky, Similarity of rectangles: An analysis of subjective dimensions Journal of Mathematical Psychology, 12(1), 1975.
[27] R. Krishnapuram and K. Kummamuru., Automatic taxonomy generation: Issues and possibilities. In Proc. IFSA 2003, LNCS, pages 184-195, 2003.
[28] N. Le Novère, M. Hucka, H. Mi., S. Moodie, and et al. The systems biology graphical notation. Nat Biotechnol, 27(8), 2009.
[29] J. P. Lewis and R. Rosenholtz., VisualIDs: Automatic distinctive icons for desktop interfaces ACM Transactions on Graphics, 23(3): 416-423, 2004.
[30] B. Love, J. Rouder, and E. Wisniewski, A structural account of global and local processing Cognitive psychology, 38(2): 291-607, 1999.
[31] S. Luck and S. Hillyard, Electrophysiological correlates of feature analysis during visual search Psychophysiology, 31(3): 291-308, 1994.
[32] E. Maguire. Taxonomy in biology and visualization. Taxonomy.pdf, Jan 2012.
[33] S. McDougall,O. De Bruijn,, and M. Curry., Exploring the effects of icon characteristics on user performance: The role of icon concreteness, complexity, and distinctiveness. Journal of Experimental Psychology: Applied, 6(4), 2000.
[34] P. Muter and D. Mayson, The role of graphics in item selection from menus Behaviour and Information Technology, 5: 89-95, 1986.
[35] D. Navon, Forest before trees: The precedence of global features in visual perception Cognitive psychology, 9(3): 353-736, 1977.
[36] D. A. Norman, The Design of Everyday Things. Basic Books, 2002.
[37] H. Ogata, S. Goto, K. Sato., W. Fujibuchi, H. Bono,, and M. Kanehisa., Kegg: Kyoto encyclopedia of genes and genomes. NAR, 27(1), 1999.
[38] S. Palmer, Hierarchical structure in perceptual representation Cognitive Psychology, 9(4): 441-915, 1977.
[39] D. Parkhurst, K. Law, and E. Niebur, Modeling the role of salience in the allocation of overt visual attention Vision research, 42(1), 2002.
[40] J. K. Patel and C. B. Read, Handbook of the Normal Distribution. Marcel Dekker, 2nd edition, 1996.
[41] J. W. Pellegrino,R. R. Rosinski,H. L. Chiesi,, and A. Siegel., Picture-word differences in decision latency: An analysis of single and dual memory models Memory and Cognition, 5: 383-396, 1977.
[42] F. Post, T. Walsum, F. Post,, and D. Silver., Iconic techniques for feature visualization. Proc. IEEE Visualization, pages 288-295, 1995.
[43] P. Quinlan, Visual feature integration theory: past, present, and future Psychological bulletin, 129(5): 643-716, 2003.
[44] P. Quinlan and G. Humphreys, Visual search for targets defined by combinations of color, shape, and size: an examination of the task constraints on feature and conjunction searches Perception & Psychophysics, 41(5): 455-527, 1987.
[45] W. Ribarsky, E. Ayers, and J. Eble, Glyphmaker: creating customized visualizations of complex data Computer, 27: 57-64, 1994.
[46] P. Rocca-Serra, E. Maguire, S.-A. Sansone,, and et al. Isa software suite: supporting standards-compliant experimental annotation and enabling cu-ration at the community level. Bioinformatics, 26(18), 2010.
[47] Rohrer and et al. The shape of shakespeare: visualizing text using implicit surfaces. In Proc. IEEE Visualization, pages 121-129, 1998.
[48] T. Ropinski, S. Oeltze, and B. Preim, Survey of glyph-based visualization techniques for spatial multivariate medical data Computers & Graphics, 35(2): 392-401, 2011.
[49] D. E. Rumelhart., A multicomponent theory of the perception of briefly exposed visual displays Journal of Math. Psych., 7(2): 191-218, 1970.
[50] S.-A. Sansone,P. Rocca-Serra, D. Field, E. Maguire,, and et al. Toward interoperable bioscience data. Nat Genet, 44(2): 121-6, 2012.
[51] R. Shepard, Attention and the metric structure of the stimulus space Journal of Mathematical Psychology, 1(1): 54-141, 1964.
[52] H. Siirtola, The effect of data-relatedness in interactive glyphs In Proc. 9th Int. Conf Information Visualization, 2002.
[53] G. Sperling, The information available in brief visual presentations Psychological Monographs: General and Applied, 74(11): 1-29, 1960.
[54] K. T. Spoehr and S. W. Lehmkuhle, Visual Information Processing. W. H. Freeman & Company, 1982.
[55] A. Treisman, Focused attention in the perception and retrieval of multidimensional stimuli Attention, Perception, & Psychophysics, 22(1), 1977.
[56] A. Treisman and S. Gormican, Feature analysis in early vision: evidence from search asymmetries Psychol Rev, 95(1): 15-48, Jan 1988.
[57] Q. Wang, P. Cavanagh, and M. Green, Familiarity and pop-out in visual search Percept Psvchophvs, 56(5): 495-500, Nov 1994.
[58] M. O. Ward., Multivariate data glyphs: Principles and practice. In Handbook of Data Visualization, pages179-198. 2008.
[59] C. Ware., Information Visualization: Perception for Design. Morgan Kaufmann, 2004
[60] S. Wiedenbeck, The use of icons and labels in an end user application program: an empirical study of learning and retention Behavior and Information Technology, 18(2): 68-82, 1999.
[61] L. Williams, The effects of target specification on objects fixated during visual search Acta psychologica, 27: 355-415, 1967.
[62] C. Wittenbrink, A. Pang, and S. Lodha, Glyphs for visualizing uncertainty in vector field IEEE Transactions on Visualization and Computer Graphics, 2: 266-279, 1996.
[63] J. Wolfe, K. Cave, and S. Franzel, Guided search: an alternative to the feature integration model for visual search Journal of experimental psychology. Human perception and performance, 15(3): 419-452, 1989.
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