Abstract
Pixel-based visualization techniques have proven to be of high value in visual data exploration, since mapping data points to pixels not only allows the analysis and visualization of large data sets, but also provides an intuitive way to convert raw data into a graphical form that often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem solving and gaining deeper domain knowledge. But the ever increasing mass of information leads to new challenges on pixel-based techniques and concepts, since the volume, complexity and dynamic nature of today’s scientific and commercial data sets are beyond the capability of many of current presentation techniques. Most existing pixel based approaches do not scale well on such large data sets as visual representation suffers from the high number of relevant data points, that might be even higher than the available monitor resolution and does therefore not allow a direct mapping of all data points to pixels on the display. In this paper we focuses on ways to increase the scalability of pixel based approaches by integrating relevance driven techniques into the visualization process. We provide first examples for effective scalable pixel based visualizations of financial- and geo-spatial data.
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Keim, D.A., Schneidewind, J., Sips, M. (2007). Scalable Pixel Based Visual Data Exploration. In: Lévy, P.P., et al. Pixelization Paradigm. VIEW 2006. Lecture Notes in Computer Science, vol 4370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71027-1_2
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DOI: https://doi.org/10.1007/978-3-540-71027-1_2
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