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From raw data to rich visualization: Combining visual search with data analysis

: May, Thorsten; Nazemi, Kawa; Kohlhammer, Jörn


Wahlster, Wolfgang (Hrsg.); Grallert, H.-J.; Wess, S.; Friedrich, H.; Widenka, T.:
Towards the Internet of Services. The THESEUS Research Program
Cham: Springer International Publishing, 2014 (Cognitive technologies)
ISBN: 978-3-319-06754-4 (Print)
ISBN: 978-3-319-06755-1 (Online)
DOI: 10.1007/978-3-319-06755-1
Book Article
Fraunhofer IGD ()
Visual analytics; information visualization; data mining

Visual analytics is an interdisciplinary field of research at the boundary between data mining, statistics and visualization. Patterns and relations in the data complement a semantic representation of knowledge on a lower level of abstraction. One important goal of visual analytics is to find relations hidden in vast amounts of data, which can be turned into useful knowledge. Analysis needs to be "visual", because human's visual cognitive abilities are important for the identification and refinement of the analytical process. Further the results of the analysis have to be presented in a way to match the user's perspective on the proposed task. However, typical users are not experts in statistics or data mining. The challenge of visual analytics is to keep domain experts in charge of the analytical process while reducing the workload due to the complexity of the techniques. While search and analysis usually are mentioned in different contexts, they are highly interdependent processes. In fact, every exploratory analysis is a search for new knowledge. In turn, this knowledge can be used to refine future searches by introducing new concepts or relations to draw from. This article will show how automated and visual methods can be combined to connect knowledge artifacts on multiple levels of abstraction.