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Interaction taxonomy for tracking of user actions in visual analytics applications

: Landesberger, Tatiana von; Fiebig, Sebastian; Bremm, Sebastian; Kuijper, Arjan; Fellner, Dieter W.


Huang, Weidong (Ed.):
Handbook of human centric visualization
New York, NY: Springer, 2014
ISBN: 1-4614-7484-1
ISBN: 978-1-4614-7484-5
ISBN: 978-1-4614-7485-2
Aufsatz in Buch
Fraunhofer IGD ()
interaction analysis; taxonomy; user behavior; visual analytics; graph visualization; Forschungsgruppe Visual Search and Analysis (VISA); Forschungsgruppe Semantic Models, Immersive Systems (SMIS); Business Field: Visual decision support; Research Area: Human computer interaction (HCI)

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 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.