On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

  • Cagatay Turkay
  • Fleur Jeanquartier
  • Andreas Holzinger
  • Helwig Hauser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8401)


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.


Visualization Visual Analytics Heterogenous Data Complex Data Future Challenges Open Problems 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Cagatay Turkay
    • 1
  • Fleur Jeanquartier
    • 2
  • Andreas Holzinger
    • 2
  • Helwig Hauser
    • 3
  1. 1.giCentre, Department of Computer ScienceCity UniversityLondonUK
  2. 2.Research Unit HCI, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
  3. 3.Visualization Group, Department of InformaticsUniversity of BergenNorway

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