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Adapting user interfaces by analyzing data characteristics for determining adequate visualizations

 
: Nazemi, Kawa; Burkhardt, Dirk; Praetorius, Alexander; Breyer, Matthias; Kuijper, Arjan

:

Kurosu, M.:
Human centered design. Second international conference, HCD 2011 : Held as part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011; Proceedings
Berlin: Springer, 2011 (Lecture Notes in Computer Science 6776)
ISBN: 3-642-21752-4
ISBN: 978-3-642-21752-4
ISBN: 978-3-642-21753-1
ISSN: 0302-9743
S.566-575
International Conference on Human Centered Design (HCD) <2, 2011, Orlando/Fla.>
International Conference on Human-Computer Interaction (HCI International) <14, 2011, Orlando/Fla.>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Adaptive visualization; human-centered user interface; human-computer interface; semantic visualization

Abstract
Today the information visualization takes in an important position, because it is required in nearly every context where large databases have to be visualized. For this challenge new approaches are needed to allow the user an adequate access to these data. Static visualizations are only able to show the data without any support to the users, which is the reason for the accomplished researches to adaptive user-interfaces, in particular for adaptive visualizations. By these approaches the visualizations were adapted to the users' behavior, so that graphical primitives were change to support a user e.g. by highlighting user-specific entities, which seems relevant for a user. This approach is commonly used, but it is limited on changes for just a single visualization. Modern heterogeneous data providing different kinds of aspects, which modern visualizations try to regard, but therefore a user often needs more than a single visualization for making an information retrieval. In this paper we describe a concept for adapting the user-interface by selecting visualizations in dependence to automatically generated data characteristics. So visualizations will be chosen, which are fitting well to the generated characteristics. Finally the user gets an aquatically arranged set of visualizations as initial point of his interaction through the data.

: http://publica.fraunhofer.de/dokumente/N-179550.html