Designing Pixel-Oriented Visualization Techniques: Theory and Applications

By Daniel A. Keim. IEEE Transactions on Visualization and Computer Graphics, 2000

486 citations recorded by the source corpus.

Visualization techniques are of increasing importance in exploring and analyzing large amounts of multidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional data sets is the class of pixel-oriented techniques. The basic idea of pixel-oriented visualization techniques is to represent as many data objects as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. A number of different pixel-oriented visualization techniques have been proposed in recent years and it has been shown that the techniques are useful for visual data exploration in a number of different application contexts. In this paper, we discuss a number of issues which are important in developing pixel-oriented visualization techniques. The major goal of this article is to provide a formal basis of pixel-oriented visualization techniques and show that the design decisions in developing them can be seen as solutions of well-defined optimization problems. This is true for the mapping of the data values to colors, the arrangement of pixels inside the subwindows, the shape of the subwindows, and the ordering of the dimension subwindows. The paper also discusses the design issues of special variants of pixel-oriented techniques for visualizing large spatial data sets.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1af08944ccddf031bcbec9befb251cb62a30b162

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Citations in the corpus

Visual exploration of large data sets (2001, background): “• Dense pixel displays that visualize each dimension value as a color pixel and group the pixels belonging to each dimension into an adjacent area [6].”
Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines (2010, background): “New visualization techniques, such as dense pixel displays [52], have been proposed for dealing with large datasets, but most of these approaches still attempt to draw each item in the dataset.”
Visualizing Data using t-SNE (2008, methodology): “Important techniques include iconographic displays such as Chernoff faces (Chernoff, 1973), pixel-based techniques (Keim, 2000), and techniques that represent the dimensions in the data as vertices in a graph (Battista et al.”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, unknown): “Methods focused on the pixels [128].”
Research directions in data wrangling: Visualizations and transformations for usable and credible data (2011, unknown): “2011–6:17pm] [1–18] K:/IVI/IVI 415994.”
VizLinter: A Linter and Fixer Framework for Data Visualization (2021, methodology): “Existing research also covers the optimization for pixel-based charts [28], radial charts [51], node-link diagrams [57], matrices [6], treemaps [48], geographical maps [8], and text-based charts [45].”
From Visual Data Exploration to Visual Data Mining: A Survey (2003, methodology): “In pixel-based techniques, a pixel is used to represent data values: Different attributes are exhibited in different subwindows and the range of possible data values are mapped to pixels according to a fixed color map [53], [55], [56] (Fig.”
A Taxonomy of Clutter Reduction for Information Visualisation (2007, background): “…23, 33, 54] point/line displacement [3, 29, 46, 47, 50] topological distortion [1, 11, 31, 34, 42] space-filling [4, 22, 44] pixel-plotting [26, 30, 41] spatial distortion dimensional reordering [38] temporal animation [14, 27, 47] spatial clustering (in Section 5 we see that clustering on…”
Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations (2009, background): “, [12, 22, 25]) for their “space-filling” roperties.”
Information Visualization and Visual Data Mining (2002, unknown): “3 and 4) [11] and the graph scetches as used in MGV (see Fig.”