Abstract
One of the central motivations for visual analytics research is the so-called information overload—implying the challenge for human users in understanding and making decisions in presence of too much information (Yang et al. in Decision Support Systems 35(1):89–102, 2003). Visual-interactive systems, integrated with automatic data analysis techniques, can help in making use of such large data sets (Thomas and Cook, Illuminating the path: The research and development agenda for visual analytics, 2005). Visual Analytics solutions not only need to cope with data volumes that are large on the nominal scale, but also with data that show high complexity. Important characteristics of complex data are that the data items are difficult to compare in a meaningful way based on the raw data. Also, the data items may be composed of different base data types, giving rise to multiple analytical perspectives. Example data types include research data compound of several base data types, multimedia data composed of different media modalities, etc.
In this paper, we discuss the role of data complexity for visual analysis and search, and identify implications for designing respective visual analytics applications. We first introduce a data complexity model, and present current example visual analysis approaches based on it, for a selected number of complex data types. We also outline important research challenges for visual search and analysis.
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Acknowledgements
This work has been supported by the following research programs and projects: The projects Visual Feature Space Analysis and Visual Analytics Methods for Modeling in Medical Imaging, funded by the German Research Foundation (DFG) within the Strategic Research Initiative on Scalable Visual Analytics (SPP 1335); the project VIS-SENSE funded by the European Commission’s Seventh Framework Programme (FP7 2007-2013) under grant agreement Nr. 257495; the THESEUS Programm funded by the German Federal Ministry of Economics and Technology; the German part of the project VASA funded by the German Federal Ministry of Education and Research; the PROBADO project funded by the German Research Foundation (DFG Leistungszentrum für Forschungsinformation); and the project VisInfo funded by the Leibniz Association (WGL). We are grateful for helpful collaboration with Prof. Kay Hamacher and other colleagues within research projects.
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von Landesberger, T., Schreck, T., Fellner, D.W., Kohlhammer, J. (2012). Visual Search and Analysis in Complex Information Spaces—Approaches and Research Challenges. In: Dill, J., Earnshaw, R., Kasik, D., Vince, J., Wong, P. (eds) Expanding the Frontiers of Visual Analytics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-2804-5_4
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