Professor Min Chen - References for Theoretic Research

References for Theoretic Research in Visualization

Related Topic: Theories of Visualization

This page was first constructed to provide a CG&A viewpoint article with a comprehensive list of papers on visualization taxonomies, ontologies, and models, The CG&A viewpoint article is:

M. Chen, G. Grinstein, C. R. Johnson, J. Kennedy, and M. Tory. Pathways for theoretical advances in visualization. IEEE Computer Graphics and Applications, 37(4):103-112, 2017. DOI

The references are ordered by the names of the authors. The numbering may change from time to time when new references are added. The initial set of references were compiled by Min Chen and Jessie Kennedy. We have tried our best to list as many papers as possible. Nevertheless, we appreciate that we may have missed some articles. In such cases, please accept our apologies and please email min.chen@oerc.ox.ac.uk to let us know. We are grateful for any comments and suggestions. The list was last updated on 20 March 2017.

Definitions [see the above reference]

Taxonomies and Ontologies: In scientific and scholarly disciplines, a collection of concepts are commonly organized into a taxonomy or ontology. In the former, concepts are known as taxa, which are typically arranged hierarchically using a tree structure. In the latter, concepts, often in conjunction with their instances, attributes, and other entities, are organized into a schematic network, where edges represent various relations and rules.

Principles and Guidelines: A principle is a law or rule that has to be followed, and is usually expressed in a qualitative description. A guideline describes a process or a set of actions that may lead to a desired outcome, or actions to be avoided in order to prevent an undesired outcome. The former usually implies a high degree of generality and certainty of the causality concerned, while the latter suggests that a causal relation may be subject to specific conditions.

Conceptual Models and Theoretic Frameworks: The terms of frameworks and models have broad interpretations. Here we consider that a conceptual model is an abstract representation of a real-world phenomenon, process, or system, featuring different functional components and their interactions. A theoretic framework provides a collection of measurements and basic operators and functions for working with these measurements. The former provides a description of complex causal relations in real world in a tentative manner, while the latter provides a basis for evaluating different models quantitatively.

Quantitative Laws and Theoretic Systems: A quantitative law describes a causal relation of concepts using a set of measurements and a computable function, which is confirmed under a theoretic framework. Under a theoretic framework, a conceptual model can be transformed to a theoretic system through axioms (postulated quantitative principles) and theorems (confirmed quantitative laws). Unconfirmed guidelines are thus conjectures, and contradictory guidelines are paradoxes.

Taxonomies

This section includes a variety of classification schemes in visualization. Some may not feature a hierarchical structure, but they can at least be included as a subtree in another classification scheme, and most likely, they can also be further divided to form a taxonomic tree. Some classification schemes feature a graph structure for organizing a set of relationships, and they are better to be referred to ontologies.

Ahn, J., Plaisant, C., & Shneiderman, B. (2014). A Task Taxonomy for Network Evolution Analysis. IEEE Transactions on Visualization and Computer Graphics, 20(3), 365–376. Retrieved from http://doi.ieeecomputersociety.org/10.1109/TVCG.2013.238
Amar, R., Eagan, J., & Stasko, J. (2005). Low-level components of analytic activity in information visualization. IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., 111–117. https://doi.org/10.1109/INFVIS.2005.1532136
Andrienko, G., Andrienko, N., Bak, P., Keim, D., Kisilevich, S., & Wrobel, S. (2011). A conceptual framework and taxonomy of techniques for analyzing movement. Journal of Visual Languages & Computing, 22(3), 213–232. https://doi.org/10.1016/j.jvlc.2011.02.003
Andrienko, N., & Andrienko, G. (2006). Exploratory analysis of spatial and temporal data: a systematic approach. New York. New York: Springer. https://doi.org/10.1007/3-540-31190-4
Bach, B., Pietriga, E., & Fekete, J.-D. (2014). GraphDiaries: Animated Transitions and Temporal Navigation for Dynamic Networks. IEEE Transactions on Visualization and Computer Graphics, 20(5), 740–754. https://doi.org/4077CBD5-B2C4-4EC6-9475-E3D0687E96A4
Beck, F., Burch, M., Diehl, S., & Weiskopf, D. (2014). The state of the art in visualizing dynamic graphs. In Proceedings of the Eurographics Conference on Visualization (EuroVis’ 14) - State of The Art Reports.
Bertin, J. (1983). Semiology of graphics: diagrams, networks, maps. Madison: University of Wisconsin Press.
Bier, E. A., Stone, M. C., Fishkin, K., Buxton, W., & Baudel, T. (1994). A taxonomy of see-through tools. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 358–364). inproceedings.
Borgo, R., Chen, M., Daubney, B., Grundy, E., Heidemann, G., Höferlin, B., & Xie, X. (2012). State of the art report on video-based graphics and video visualization. Computer Graphics Forum, 31(8), 2450–2477. https://doi.org/10.1111/j.1467-8659.2012.03158.x
Borgo, R., Kehrer, J., Chung, D. H. S., Maguire, E., Laramee, R. S., Hauser, H. & Chen, M. (2013). Glyph-based visualization: Foundations, design guidelines, techniques and applications. Eurographics State of the Art Reports, 39–63. https://doi.org/10.2312/conf/EG2013/stars/039-063
Bowman, D. A., & Hodges, L. F. (1999). Formalizing the design, evaluation, and application of interaction techniques for immersive virtual environments. Journal of Visual Languages & Computing, 10(1), 37–53. article.
Brehmer, M., & Munzner, T. (2013). A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2376–85. https://doi.org/10.1109/TVCG.2013.124
Brodlie, K. (1992). Visualization techniques. In P. Brodlie, K.W., Carpenter, L.A., Earnshaw, R.A., Gallop, J.R., Hubbold, R.J., Mumford, A.M., Osland, C.D., Quarendon (Ed.), Scientific visualization: techniques and applications. (pp. 37–85). Springer-Verlag.
Brodlie, K. (1993). A classification scheme for scientific visualization. In R. A. Earnshaw & D. Watson (Eds.), Animation and Scientific Visualization (pp. 125–140). Academic Press.
Buja, A., Cook, D., & Swayne, D. F. (1996). Interactive high-dimensional data visualization. Journal of Computational and Graphical Statistics, 5(1), 78–99.
Byrne, L., Angus, D., & Wiles, J. (2016). Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives. IEEE Transactions on Visualization and Computer Graphics, 22(1), 509–518.
Card, S. K., & Mackinlay, J. (1997). The structure of the information visualization design space. In Information Visualization, 1997. Proceedings., IEEE Symposium on (pp. 92–99). https://doi.org/10.1109/INFVIS.1997.636792
Card, S. K., Mackinlay, J. D., & Robertson, G. G. (1990). The design space of input devices. In Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’90 (pp. 117–124). https://doi.org/10.1145/97243.97263
Casner, S. M. (1991). Task-analytic approach to the automated design of graphic presentations. ACM Transactions on Graphics (ToG), 10(2), 111–151. article.
Chen, M., & Floridi, L. (2013). An analysis of information visualisation. Synthese, 190(16), 3421–3438. https://doi.org/10.1007/s11229-012-0183-y
Chen, M., & Jänicke, H. (2010). An information-theoretic framework for visualization. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1206–1215. https://doi.org/10.1109/TVCG.2010.132
Chen, M., Walton, S., Berger, K., Thiyagalingam, J., Duffy, B., Fang, H., & Trefethen, A. E. (2014). Visual multiplexing. Computer Graphics Forum, 33(3), 241–250. https://doi.org/10.1111/cgf.12380
Chi, E. H. (2000). A taxonomy of visualization techniques using the data state reference model. IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings, 94301, 69–75. https://doi.org/10.1109/INFVIS.2000.885092
Chuah, M. C., & Roth, S. F. (1996). On the semantics of interactive visualizations. In Proceedings IEEE Symposium on Information Visualization ’96 (pp. 29–36). https://doi.org/10.1109/INFVIS.1996.559213
Cottam, J. A., Lumsdaine, A., & Weaver, C. (2012). Watch this : A taxonomy for dynamic data visualization, In Proc. IEEE Visual Analytics Science and Technology (VAST), 193–202.
Dix, A., & Ellis, G. (1998). Starting simple: adding value to static visualisation through simple interaction. In Proceedings of the working conference on Advanced visual interfaces (pp. 124–134).
Ellis, G., & Dix, A. (2007). A taxonomy of clutter reduction for information visualisation. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1216–23. https://doi.org/10.1109/TVCG.2007.70535
Elmqvist, N., & Tsigas, P. (2007). A Taxonomy of 3D Occlusion Management Techniques. In 2007 IEEE Virtual Reality Conference (pp. 55–58).
Elmqvist, N., & Yi, J. S. (2015). Patterns for visualization evaluation. Information Visualization, 14(3), 250–269. https://doi.org/10.1177/1473871613513228
Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C. D., & Roberts, J. C. (2011). Visual comparison for information visualization. Information Visualization, 10(4), 289–309. https://doi.org/10.1177/1473871611416549
Gotz, D., & Zhou, M. X. (2009). Characterizing users’ visual analytic activity for insight provenance. Information Visualization, 8(1), 42–55. https://doi.org/http://dx.doi.org/10.1057/ivs.2008.31
Graham, M., & Kennedy, J. (2010). A survey of multiple tree visualisation. Information Visualization, 9(4), 235–252. https://doi.org/10.1057/ivs.2009.29
Hadlak, S., Schulz, H.-J., & Schumann, H. (2011). In Situ Exploration of Large Dynamic Networks. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2334–2343. https://doi.org/10.1109/TVCG.2011.213
Hadlak, S., Schumann, H., & Schulz, H.-J. (2015). A survey of multi-faceted graph visualization. In R. Borgo, F. Ganovelli, & I. Viola (Eds.), Eurographics Conference on Visualization (EuroVis) - STARs (pp. 1–2). inproceedings, The Eurographics Association. https://doi.org/10.2312/eurovisstar.20151109
Heer, J., & Robertson, G. (2007). Animated transitions in statistical data graphics. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1240–7. https://doi.org/10.1109/TVCG.2007.70539
Heer, J., & Shneiderman, B. (2012). Interactive Dynamics for Visual Analysis: A taxonomy of tools that support the fluent and flexible use of visualizations. Communications of the ACM, 55(4), 45–54. https://doi.org/10.1145/2133806.2133821
Huang, D., Tory, M., Aseniero, B. A., Bartram, L., Bateman, S., Carpendale, S., & Woodbury, R. (2015). Personal visualization and personal visual analytics. IEEE Transactions on Visualization and Computer Graphics, 21(3), 420–433. https://doi.org/10.1109/TVCG.2014.2359887
Hullman, J., & Diakopoulos, N. (2011). Visualization rhetoric: Framing effects in narrative visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2231–2240. https://doi.org/10.1109/TVCG.2011.255
Isenberg, P., Isenberg, T., Sedlmair, M., Chen, J., & Moller, T. (2016). Visualization as seen through its research paper keywords. IEEE Transactions on Visualization and Computer Graphics, 2626(c), 1–1. https://doi.org/10.1109/TVCG.2016.2598827
Javed, W., & Elmqvist, N. (2012). Exploring the design space of composite visualization. 2012 IEEE Pacific Visualization Symposium, 1–8. https://doi.org/10.1109/PacificVis.2012.6183556
Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1), 1–8. article.
Keim, D. A., Kriegel, H., & Society, E. C. (1996). Visualization techniques for mining large databases : A comparison. IEEE Transactions on Knowledge and Data Engineering, 8(6), 923–938.
Kerracher, N., Kennedy, J., & Chalmers, K. (2014). The design space of temporal graph visualisation. In N. Elmqvist, M. Hlawitschka, & J. Kennedy (Eds.), Proceedings of the Eurographics Conference on Visualization (EuroVis ’14), Short Papers Track (pp. 7–11). Swansea: The Eurographics Assocation. https://doi.org/10.2312/eurovisshort.20141149
Kerracher, N., Kennedy, J., & Chalmers, K. (2015). A task taxonomy for temporal graph visualisation. Visualization and Computer Graphics, IEEE Transactions on, 21(10), 1160–1172. article. https://doi.org/10.1109/TVCG.2015.2424889
Kerracher, N., Kennedy, J., Chalmers, K., & Graham, M. (2015). Visual techniques to support exploratory analysis of temporal graph data. In E. Bertini, J. Kennedy, & E. Puppo (Eds.), Eurographics Conference on Visualization (EuroVis) - Short Papers. inproceedings, Cagliari, Sardinia: The Eurographics Association. https://doi.org/10.2312/eurovisshort.20151133
Kersten-Oertel, M., Jannin, P., & Collins, D. L. (2012). DVV: A taxonomy for mixed reality visualization in image guided surgery. IEEE Transactions on Visualization and Computer Graphics, 18(2), 332–352. https://doi.org/10.1109/TVCG.2011.50
Kozlikova, B., Krone, M., Lindow, N., Falk, M., Baaden, M., Baum, D., … Hege, H.-C. (2015). Visualization of biomolecular structures: state of the art. In R. Borgo, F. Ganovelli, & I. Viola (Eds.), Eurographics Conference on Visualization (EuroVis) - STARs. inproceedings, The Eurographics Association. https://doi.org/10.2312/eurovisstar.20151112
Kucher, K., & Kerren, A. (2015). Text visualization techniques: taxonomy, visual Survey, and community insights. In 2015 IEEE Pacific Visualization Symposium (PacificVis) (pp. 117–121). IEEE.
Lam, H. (2008). A Framework of interaction costs in information visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1149–1156. https://doi.org/10.1109/TVCG.2008.109
Lam, H., Bertini, E., Isenberg, P., Plaisant, C., & Carpendale, S. (2012). Empirical studies in information visualization: Seven scenarios. IEEE Transactions on Visualization and Computer Graphics, 18(9), 1520–1536. https://doi.org/10.1109/TVCG.2011.279
Lammarsch, T., Rind, A., Aigner, W., & Miksch, S. (2012). Developing an extended task framework for exploratory data analysis along the structure of time. In K. Matkovic & G. Santucci (Eds.), EuroVA 2012: International Workshop on Visual Analytics (pp. 31–35). The Eurographics Association. https://doi.org/10.2312/PE/EuroVAST/EuroVA12/031-035
Lee, B., Plaisant, C., Parr, C. S., Fekete, J.-D., & Henry, N. (2006). Task taxonomy for graph visualization. In Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization (pp. 81–85), New York, NY, USA: ACM. https://doi.org/http://doi.acm.org/10.1145/1168149.1168168
Leung, Y. K., & Apperley, M. D. (1994). A review and taxonomy of distortion-oriented presentation techniques. ACM Transactions on Computer-Human Interaction (TOCHI), 1(2), 126–160.
Liu, Z., & Stasko, J. T. (2010). Mental models , visual reasoning and interaction in information visualization : A top-down perspective. IEEE Transactions on Visualization and Computer Graphics, 16(6), 999–1008. https://doi.org/10.1109/tvcg.2010.177
Maguire, E., Rocca-Serra, P., Sansone, S. A., Davies, J., & Chen, M. (2012). Taxonomy-based glyph design-With a case study on visualizing workflows of biological experiments. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2603–2612. https://doi.org/10.1109/TVCG.2012.271
Maletic, J. I., Marcus, A., & Collard, M. L. (2002). A task oriented view of software visualization. Proceedings First International Workshop on Visualizing Software for Understanding and Analysis, 32–40. https://doi.org/10.1109/VISSOF.2002.1019792
Morse, E., Lewis, M., & Olsen, K. . (2000). Evaluating visualizations: using a taxonomic guide. International Journal of Human-Computer Studies, 53(5), 637–662. https://doi.org/10.1006/ijhc.2000.0412
Mullins, P. M., & Treu, S. (1993). A task-based cognitive model for user-network interaction: defining a task taxonomy to guide the interface designer. Interacting with Computers, 5(2), 139–166. https://doi.org/10.1016/0953-5438(93)90016-M
Murray, P., Mcgee, F., & Forbes, A. G. (2016). A taxonomy of visualization tasks for the analysis of biological pathway data. In Biological Data Visualization (BioVis), 2016 IEEE Symposium on.
Pfitzner, D., Hobbs, V., & Powers, D. (2003). A unified taxonomic framework for information visualization. Proceedings of the Asia-Pacific Symposium on Information Visualisation, 24, 57–66. Retrieved from http://crpit.com/confpapers/CRPITV24Pfitzner.pdf
Plaisant, C., Carr, D., & Shneiderman, B. (1995). Image-browser taxonomy and guidelines for designers. Ieee Software, 12(2), 21–32. article.
Potter, K., Rosen, P., & Johnson, C. R. (2012). From quantification to visualization: A taxonomy of uncertainty visualization approaches. IFIP Advances in Information and Communication Technology, 377 AICT, 226–247. https://doi.org/10.1007/978-3-642-32677-6_15
Pousman, Z., & Stasko, J. (2006). A taxonomy of ambient information systems: four patterns of design. In Proceedings of the working conference on Advanced visual interfaces (pp. 67–74). ACM. https://doi.org/10.1145/1133265.1133277
Pretorius, A. J., Purchase, H. C., & Stasko, J. T. (2014). Tasks for multivariate network analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8380 LNCS, 77–95. https://doi.org/10.1007/978-3-319-06793-3_5
Rind, A., Aigner, W., Wagner, M., Miksch, S., & Lammarsch, T. (2015). Task cube: A three-dimensional conceptual space of user tasks in visualization design and evaluation. Information Visualization. Retrieved from http://mc.fhstp.ac.at/sites/default/files/publications/rind_2015_ivi_task-cube_postprint.pdf
Roberts, J. C. (2007). State of the art: coordinated & multiple views in exploratory visualization. In Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007) (pp. 61–71). Ieee. https://doi.org/10.1109/CMV.2007.20
Roth, R. E. (2013). An empirically-derived taxonomy of interaction primitives for interactive cartography and geovisualization. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2356–2365.
Roth, S. F., & Mattis, J. (1990). Data characterization for intelligent graphics presentation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 193–200). inproceedings.
Rufiange, S., & McGuffin, M. J. (2013). DiffAni: visualizing dynamic graphs with a hybrid of difference maps and animation. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2556–65. https://doi.org/10.1109/TVCG.2013.149
Rufiange, S., McGuffin, M. J., & Fuhrman, C. P. (2012). TreeMatrix: A hybrid visualization of compound graphs. Computer Graphics Forum, 31(1), 89–101. https://doi.org/10.1111/j.1467-8659.2011.02087.x
Saket, B., Simonetto, P., & Kobourov, S. (2014). Group-level graph visualization taxonomy. In Eurographics Conference on Visualization (EuroVis), Short Papers Track.
Schulz, H.-J., Hadlak, S., & Schumann, H. (2011). The design space of implicit hierarchy visualization: A survey. IEEE Transactions on Visualization and Computer Graphics, 17(4), 393–411. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/20498508
Schulz, H.-J., Nocke, T., Heitzler, M., & Schumann, H. (2013). A design space of visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2366–75. https://doi.org/10.1109/TVCG.2013.120
Schulz, H.-J., & Schumann, H. (2006). Visualizing graphs - A generalized view. In Proceedings of the International Conference on Information Visualisation (pp. 166–173). https://doi.org/10.1109/IV.2006.130
Sedig, K., & Parsons, P. (2013). Interaction design for complex cognitive activities with visual representations: A pattern-based approach. AIS Transactions on Human-Computer Interaction, 5(1), 84–133.
Sedlmair, M., Tatu, A., Munzner, T., & Tory, M. (2012). A taxonomy of visual cluster separation factors. Computer Graphics Forum, 31(3pt4), 1335–1344. https://doi.org/10.1111/j.1467-8659.2012.03125.x
Segel, E., & Heer, J. (2010). Narrative visualization: telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139–48. https://doi.org/10.1109/TVCG.2010.179
Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations. In Visual Languages, 1996. Proceedings., IEEE Symposium on (pp. 336–343). CONF. Retrieved from http://doi.ieeecomputersociety.org/10.1109/VL.1996.545307
Springmeyer, R. R., Blattner, M. M., & Max, N. L. (1992). A characterization of the scientific data analysis process. Proceedings of the 3rd Conference on Visualization’92, 235–242. https://doi.org/10.1109/VISUAL.1992.235203
Tory, M., & Moller, T. (2004). Rethinking visualization: A high-level taxonomy. IEEE Symposium on Information Visualization, 151–158. https://doi.org/10.1109/INFVIS.2004.59
Tweedie, L. (1997). Characterizing interactive externalizations. In Proceedings of the ACM SIGCHI Conference on Human factors in computing systems (pp. 375–382). https://doi.org/10.1145/258549.258803
Valiati, E. R. A., Pimenta, M. S., & Freitas, C. M. D. S. (2006). A taxonomy of tasks for guiding the evaluation of multidimensional visualizations. In E. Bertini, C. Plaisant, & G. Santucci (Eds.), Proceedings of the 2006 AVI workshop on BEyond time and errors novel evaLuation methods for Information Visualization - BELIV ’06. New York, New York, USA: ACM Press. https://doi.org/10.1145/1168149.1168169
Vehlow, C., Beck, F., & Weiskopf, D. (2015). The state of the art in visualizing group structures in graphs. Eurographics Conference on Visualization (EuroVis15). https://doi.org/10.2312/eurovisstar.20141174
von Landesberger, T., Fiebig, S., Bremm, S., Kuijper, A., & Fellner, D. W. (2014). Interaction taxonomy for tracking of user actions in visual analytics applications. In W. Huang, P. Parsons, & K. Sedig (Eds.), Handbook of Human Centric Visualization (pp. 653–670). Springer. https://doi.org/10.1007/978-1-4614-7485-2
Ward, M., & Yang, J. (2004). Interaction Spaces in Data and Information Visualization. In VisSym (pp. 137–145). https://doi.org/10.2312/VisSym/VisSym04/137-146
Wehrend, S., & Lewis, C. (1990). A problem-oriented classification of visualization techniques. Proceedings of the First IEEE Conference on Visualization: Visualization `90, 139–143,. https://doi.org/10.1109/VISUAL.1990.146375
Wybrow, M., Elmqvist, N., Fekete, J.-D., Landesberger, T. Von, Wijk, J. J. van, & Zimmer, B. (2014). Interaction in the visualization of multivariate networks. In Multivariate Network Visualization, LNCS 8380 (pp. 97–125).
Yi, J. S., Kang, Y. A., Stasko, J., & Jacko, J. (2007). Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1224–31. https://doi.org/10.1109/TVCG.2007.70515
Zhao, S., McGuffin, M. J., & Chignell, M. H. (2005). Elastic hierarchies: combining treemaps and node-link diagrams. In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005. (pp. 57–64). Minneapolis, MN, USA: IEEE Computer Society Press. https://doi.org/10.1109/INFVIS.2005.1532129
Zhou, M. X., & Feiner, S. K. (1998). Visual task characterization for automated visual discourse synthesis. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI ’98, 392–399. https://doi.org/10.1145/274644.274698

Ontologies

Ontologies are representations of different relationships among various concepts. Because ontology is a less-used term, authors often refer to a collection of ontological relationships as a model. So please also check the references for Conceptual Models.

Brodlie, K. (1993). A classification scheme for scientific visualization. In R. A. Earnshaw and D.Watson, editors, Animation and Scientific Visualization, pages 125–140. Academic Press.
Brodlie, K., Duce, D., & Duke, D. (2004). Visualization ontologies: Report of a workshop held at the national e-science centre. Report e-Science Institute.
Card, S. & Mackinlay, J. (1997). The structure of the information visualization design space. In Proc. IEEE Information Visualization, pages 92–99.
Duke, D. J., Brodlie, K. W., & Duce, D. A. (2004). Building an ontology of visualization. In IEEE Visualization 2004 Conference, Extended Abstract, pp. 7–8.
Duke, D. J., Brodlie, K. W., Duce, D. A., & Herman, I. (2005). Do you see what I mean? IEEE Computer Graphics and Applications, 25(3):6–9.
Pérez, A. M. C., Risquet, P. & Gómez, J. M. (2010). An enhanced visualization ontology for a better representation of the visualization process. ICT Innovations, 83:342–347.
Polowinski, J. & Voigt, M. (2013). VISO: a shared, formal knowledge base as a foundation for semi-automatic infovis systems. ACM SIGCHI Conference on
Human Factors in Computing Systems (CHI), pp. 1791–1796.
Sacha, D., Kraus, M., Keim, D. A., & Chen, M. (2019). VIS4ML: An ontology for visual analytics assisted machine learning. IEEE Transactions on Visualization and Computer Graphics, 25(1).
Shu, G., Avis, N. J., & Rana, O. (2006). Investigating visualization ontologies. In Proc. of the UK e-Science All Hands Meeting.
Voigt, M. & Polowinski, J. (2011). Towards a unifying visualization ontology. Technical Report, Technische Universität Dresden.

Conceptual Models

A conceptual model can be a representation of an idea, a process, or a system. In the field of visualization, a number of models were proposed to describe the relationships among data, visualization systems, analytical techniques, interaction methods, human perception and cognition, user tasks, and application contexts. The goal of such models is to help us describe, understand, reason, and predict what people may do in a visualization process and environment, In order to enable prediction, a conceptual model has to feature causal relationships. Some models that do not feature causal relationships may actually belong to the category of ontologies.

Ahmed, N., Zhang, Z., Mueller, K. (2012). Human computation in visualization: using purpose-driven games for robust evaluation of visualization algorithms. IEEE TVCG 18(2).
Brehmer, M. and Munzner, T. (2013). A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12):2376–2385.
Chen, M., Ebert, D., Hagen, H, Laramee, R. S., van Liere, R., Ma, K.-L., Ribarsky, W., Scheuermann, G. and Silver, D. (2009). Data, Information and Knowledge in Visualization, IEEE Computer Graphics and Applications, 29(1):12-19
Chen, M. and Golan, A. (2016). What May Visualization Processes Optimize? IEEE Transactions on Visualization and Computer Graphics, 22(12):2619-2632.
Demiralp, C., Scheidegger, C. E., Kindlmann, G. L., Laidlaw, D. H., and Heer, J. (2014). Visual embedding: A model for visualization. IEEE Computer Graphics and Applications, 34(1):10–15.
Endert, A., Fiaux, P., and North, C. (2012). Semantic interaction for sensemaking: inferring analytical reasoning for model steering. IEEE Transactions on Visualization and Computer Graphics, 18(12):2879–2888.
Green, T. M., Ribarsky, W., and Fisher, B. (2009). Building and applying a human cognition model for visual analytics. Information Visualization, 8(1):113.
Huang, D., Tory, M., and Bertram, L. (2016). A field study of on-calendar visualizations. In Proc. Graphics Interface 2016.
Jankun-Kelly, T., Ma, K.-L., and Gertz, M. (2007). A model and framework for visualization exploration. IEEE Transactions on Visualization and Computer Graphics, 13(6):357–369.
Jansen, J. and Dragicevic, P. (2013). An interaction model for visualizations beyond the desktop. IEEE TVCG, 19(12).
Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., and Melançon, G. (2008). Visual Analytics: Definition, Process, and Challenges. LNCS 4950.
Liu, Z., Nersessian, N., and Stasko, J. (2008). Distributed cognition as a theoretical framework for information visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6):1173–1180.
K. Moreland (2013). A survey of visualization pipeline, IEEE TVCG, 19(3).
Munzner, T. (2009). A nested model for visualization design and validation. IEEE Trans. Visualization & Comp. Graphics 15, 6, 921–928.
Patterson, R. E., Blaha, L., Grinstein, G., Liggett, K., Kaveney, D. E., Sheldon, K., Havig, P. R., & Moore, J. A. (2014). A human cognition framework for information visualization, Computer & Graphics, 42: 42-58.
Pirolli, P., and Card, S. (2005). The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proc. Int. Conf. on Intelligence Analysis, vol. 5, pp. 2–4.
Purchase, H. C., Andrienko, N., Jankun-Kelly, T., and Ward, M. (2008). Theoretical foundations of information visualization. In Information Visualization: Human-Centered Issues and Perspectives, Springer LNCS 4950, pages 46–64.
Sacha, D., Stoffel, A., Stoffel, F., Kwon, B. C., Ellis, G., and Keim, D. A. (2014). Knowledge generation model for visual analytics. IEEE Transactions on Visualization and Computer Graphics, 20(12):1604–1613, 2014.
Silver, D. (1995). Object-oriented visualization. IEEE Computer Graphics and Applications, 15(3):54–62.
Vickers, P., Faith, J., and Rossiter, N. (2013). Understanding visualization: A formal approach using category theory and semiotics. IEEE Transactions on Visualization and Computer Graphics, 19(6):1048–1061.
Walsum, T. V., Post, F. H., Silver, D, and Post, F. J. (1996). Feature extraction and iconic visualization. IEEE Transactions on Visualization and Computer Graphics, 2(2):111–119.
Wang, X., Dou, W., Butkiewicz, T., Bier,, E. A., Ribarsky, W. (2011). A two-stage framework for designing visual analytics system in organizational environment, Proc. IEEE VAST.
van Wijk, J. J. (2005). The value of visualization. In Proc. IEEE Visualization, pages 79–86.
Wood, J., Wright, H, and Brodlie, K. (1997). Collaborative visualization, Proc. IEEE Visualization.

Theoretic Frameworks

A theoretic framework provides a collection of measurements and basic operators and functions for working with these measurements.

Chen, M., Feixas, M., Viola, I., Bardera, A., Shen, H.-W., and Sbert, M. (2016). Information Theory Tools for Visualization. A K Peters/CRC Press. ISBN: 9781498740937 - CAT# K26715.
Chen, M. and Jaenicke, H. (2010). An information-theoretic framework for visualization, IEEE Transactions on Visualization and Computer Graphics, 16(6):1206-1215.
Feixas, M., Sbert, M., and Gonzalez. F. (2009). A unified information-theoretic framework for viewpoint selection and mesh saliency. ACM Transactions on Applied Perception, 6(1):1:1{1:23.
Kindlmann, G. and Scheidegger, C. (2014). An algebraic process for visualization design. IEEE Transactions on Visualization and Computer Graphics, 20(12): 2181–2190.
Xu, L., Lee, T.-Y., and Shen, H.-W. (2010). An information-theoretic framework for fow visualization, IEEE Transactions on Visualization and Computer Graphics, 16(6): 1216-1224.

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