Modeling Interactive, 3-Dimensional Information Visualizations Supporting Information Seeking Behaviors

Information visualization and knowledge visualization use comparable techniques and methods. Based on mapping rules, resource objects are translated into visual objects as meaningful representations, offering easy and comprehensive access. Whereas information visualization displays data objects and relations, knowledge visualization maps knowledge elements and ontologies. Bridging this gap must start at concept level. Our approach is to design a declarative language for describing and defining information visualization techniques. The information visualization modeling language (IVML) provides a means to formally represent, note, preserve, and communicate structure, appearance, behavior, and functionality of information visualization techniques and applications in a standardized way. The anticipated benefits comprise both application and theory.

[1]  David A. Carr,et al.  A cognitive classification framework for 3-dimensional information visualization , 1998 .

[2]  Min Chen,et al.  Building a visual database for example-based graphics generation , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[3]  Cristina Russo Dos Santos,et al.  Multiple views in 3D metaphoric information visualization , 2002, Proceedings Sixth International Conference on Information Visualisation.

[4]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[5]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[6]  Constantine Stephanidis,et al.  Human-computer interaction : theory and practice , 2003 .

[7]  Chris North,et al.  Visualization schemas for flexible information visualization , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[8]  Ed H. Chi,et al.  A taxonomy of visualization techniques using the data state reference model , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[9]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[10]  Virginia Teller.  Review of Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition by Daniel Jurafsky and James H. Martin. Prentice Hall 2000. , 2000 .

[11]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[12]  Mark D. Apperley,et al.  A review and taxonomy of distortion-oriented presentation techniques , 1994, TCHI.

[13]  B. Partee,et al.  Mathematical Methods in Linguistics , 1990 .

[14]  Sigmar-Olaf Tergan,et al.  Managing knowledge with computer-based mapping tools , 2003 .

[15]  Allison Woodruff,et al.  Guidelines for using multiple views in information visualization , 2000, AVI '00.

[16]  Frank van Harmelen,et al.  Ontology-based Information Visualization , 2002, Visualizing the Semantic Web.

[17]  John Anthony Roberts.  Multiple view and multiform visualization , 2000, Electronic Imaging.

[18]  Steven F. Roth,et al.  On the semantics of interactive visualizations , 1996, Proceedings IEEE Symposium on Information Visualization '96.

[19]  Neff Walker,et al.  A classification of visual representations , 1994, CACM.

[20]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[21]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .