On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics
Abstract
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 Humanââ¬âComputer 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.
C. Turkay, F. Jeanquartier, A. Holzinger, and H. Hauser, "On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics," in Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, Springer, 2014, p. 117–140. doi:10.1007/978-3-662-43968-5_7)
[BibTeX]
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 Humanââ¬âComputer 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.
@INCOLLECTION {turkay2014computationally,
author = "Turkay, Cagatay and Jeanquartier, Fleur and Holzinger, Andreas and Hauser, Helwig",
title = "On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics",
booktitle = "Interactive Knowledge Discovery and Data Mining in Biomedical Informatics",
publisher = "Springer",
year = "2014",
pages = "117--140",
abstract = "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 Humanââ¬âComputer 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.",
images = "images/img_Page_12_Image_0001.jpg, images/img_Page_12_Image_0002.jpg, images/img_Page_12_Image_0003.jpg",
thumbnails = "images/img_Page_12_Image_0001.jpg",
doi = "10.1007/978-3-662-43968-5_7)"
}