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Verbalization in search: Implication for the need of adaptive visualizations

: Nazemi, Kawa; Christ, Oliver

Ji, Yong Gu (Ed.):
4th International Conference on Applied Human Factors and Ergonomics, AHFE 2012. Vol.1: Advances in affective and pleasurable design : 21 - 25 July 2012, San Francisco, Calif.; proceedings
Boca Raton, Fla.: CRC Press, 2012 (Advances in human factors and ergonomics series)
ISBN: 978-1-4398-7118-8
ISBN: 978-1-4665-5262-3
ISBN: 978-1-4398-7119-5 (eBook)
International Conference on Applied Human Factors and Ergonomics (AHFE) <4, 2012, San Francisco/Calif.>
Conference Paper
Fraunhofer IGD ()
Adaptive visualization; semantic visualization; exploratory visualization; evaluation; Business Field: Digital society; Business Field: Visual decision support; Research Area: Generalized digital documents

Interactive information visualization enables human to interact with huge and complex data and gather implicit information. Different visualization strategies allow solving visualization tasks e.g., exploring information, making decision or searching explicit information. The process of searching information premises the human verbalization ability. The solving probability of a search problem increases with the precise ability of formulating the query. The query formulation depends on pre-knowledge and verbalization ability of the subject. In this paper we show that, beside the interactive information visualization technique like bottom up or top down, the need of additional ideas e.g. adaptive visualization to increase the verbalization abilities of subjects should be implemented. This is endorsed by an evaluation study of users with significant differences in previous subjective ratings of high or low values of self-assurance in working with personal computers. The results of this evaluation let us assume that personalized or adaptive visualization will help to enhance the verbalization ability and therewith the search and exploration efficiency in subjects with low values of self-assurance. The paper concludes with a short description of an adaptive semantics visualization model.