On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics

Turkay, C., Jeanquartier, F., Holzinger, A. & Hauser, H. (2014). On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics. Lecture Notes in Computer Science, 8401, pp. 117-140. doi: 10.1007/978-3-662-43968-5_7

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With the advance of new data acquisition and generation technologies, the biomedical domain is becoming increasingly data-driven. Thus, understanding the information in large and complex data sets has been in the focus of several research fields such as statistics, data mining, machine learning, and visualization. While the first three fields predominantly rely on computational power, visualization relies mainly on human perceptual and cognitive capabilities for extracting information. Data visualization, similar to Human–Computer Interaction, attempts an appropriate interaction between human and data to interactively exploit data sets. Specifically within the analysis of complex data sets, visualization researchers have integrated computational methods to enhance the interactive processes. In this state-of-the-art report, we investigate how such an integration is carried out. We study the related literature with respect to the underlying analytical tasks and methods of integration. In addition, we focus on how such methods are applied to the biomedical domain and present a concise overview within our taxonomy. Finally, we discuss some open problems and future challenges.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-662-43968-5_7
Uncontrolled Keywords: 08 Information And Computing Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Informatics > Department of Computing
School of Informatics > giCentre
URI: http://openaccess.city.ac.uk/id/eprint/14233

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