Abstract
In various application areas (social science, transportation, or medicine) analysts need to gain knowledge from large amounts of data. This analysis is often supported by interactive Visual Analytics tools that combine automatic analysis with interactive visualization. Such a data analysis process is not streamlined, but consists of several steps and feedback loops. In order to be able to optimize the process, identify problems, or common problem solving strategies, recording and reproducibility of this process is needed. This is facilitated by tracking of user actions categorized according to a taxonomy of interactions.Visual Analytics includes several means of interaction that are differentiated according to three fields: information visualization, reasoning, and data processing. At present, however, only separate taxonomies for interaction techniques exist in these three fields. Each taxonomy covers only a part of the actions undertaken in Visual Analytics. Moreover, as they use different foundations (user intentions vs. user actions) and employ different terminology, it is not clear to what extent they overlap and cover the whole Visual Analytics interaction space. We therefore first compare them and then elaborate a new integrated taxonomy in the context of Visual Analytics.In order to show the usability of the new taxonomy, we specify it on visual graph analysis and apply it to the tracking of user interactions in this area.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bertini, E., Lalanne, D.: Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery, pp. 12–20 (2009)
Card, S.C., Mackinlay, J., Shneiderman, B.: Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers (1999)
Chen, Y., Barlowe, S., Yang, J.: Click2annotate: Automated insight externalization with rich semantics. In: IEEE Symposium on Visual Analytics Science and Technology, pp. 155–162. IEEE (2010)
Gotz, D., Zhou, M.: Characterizing users’ visual analytic activity for insight provenance. In: Proceedings of IEEE Symposium on Visual Analytics Science and Technology, pp. 123–130 (2008)
Heer, J., Mackinlay, J., Stolte, C., Agrawala, M.: Graphical histories for visualization: Supporting analysis, communication, and evaluation. Visualization and Computer Graphics, IEEE Transactions on 14(6), 1189–1196 (2008)
Heer, J., Shneiderman, B.: Interactive dynamics for visual analysis. Queue 10(2), 30 (2012)
Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering The Information Age-Solving Problems with Visual Analytics. EuroGraphics (2010)
Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H.: Visual analytics: Scope and challenges, visual data mining: Theory, techniques and tools for visual analytics. Lecture Notes In Computer Science (lncs) 4404, 76–90 (2008)
von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J., Fekete, J.D., Fellner, D.: Visual analysis of large graphs: State-of-the-art and future research challenges. Computer Graphics Forum 30(6), 1719–1749 (2011)
Lipford, H., Stukes, F., Dou, W., Hawkins, M., Chang, R.: Helping users recall their reasoning process. Proc. VAST’10 pp. 187–194 (2010)
Thomas, J.J., Cook, K.A.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. National Visualization and Analytics Center (2005)
US National Library of Medicine National Institutes of Health: PubMed database. online. URL http://www.ncbi.nlm.nih.gov/pubmed/
Ware, C.: Information visualization: Perception for Design. Morgan Kaufmann (2000)
Yi, J.S., Kang, Y.a., Stasko, J., Jacko, J.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics 13(6), 1224–1231 (2007). DOI http://dx.doi.org/10.1109/TVCG.2007.70515
Acknowledgements
The authors would like to thank DFG for the financial support within SPP Scalable Visual Analytics Programme (SPP 1335). We are thankful to our partners within the THESEUS program for providing us with the data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
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: Huang, W. (eds) Handbook of Human Centric Visualization. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7485-2_26
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7485-2_26
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7484-5
Online ISBN: 978-1-4614-7485-2
eBook Packages: Computer ScienceComputer Science (R0)