Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Fidelity (G5) <s> How data are represented visually has a powerful effect on how the structure in those data is perceived. For example, in Figure 1, four representations of an MRI scan of a human head are shown. The only difference between these images is the mapping of color to data values, yet, the four representations look very different. Furthermore, the inferences an analyst would draw from these representations would vary considerably. That is, variations in the method of representing the data can significantly influence the user's perception and interpretation of the data. How NOT to Lie with Visualization http://www.research.ibm.com/dx/proceedings/pravda/truevis.htm <s> BIB001 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Fidelity (G5) <s> Data abstraction techniques are widely used in multiresolution visualization systems to reduce visual clutter and facilitate analysis from overview to detail. However, analysts are usually unaware of how well the abstracted data represent the original dataset, which can impact the reliability of results gleaned from the abstractions. In this paper, we define two data abstraction quality measures for computing the degree to which the abstraction conveys the original dataset: the histogram difference measure and the nearest neighbor measure. They have been integrated within XmdvTool, a public-domain multiresolution visualization system for multivariate data analysis that supports sampling as well as clustering to simplify data. Several interactive operations are provided, including adjusting the data abstraction level, changing selected regions, and setting the acceptable data abstraction quality level. Conducting these operations, analysts can select an optimal data abstraction level. Also, analysts can compare different abstraction methods using the measures to see how well relative data density and outliers are maintained, and then select an abstraction method that meets the requirement of their analytic tasks <s> BIB002 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Fidelity (G5) <s> Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions - illustrated in many examples - for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA. <s> BIB003 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Fidelity (G5) <s> In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility. <s> BIB004 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Fidelity (G5) <s> We present the zoomable adjacency matrix explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higher-order meta-representations. Aggregates are arranged into a pyramid hierarchy that allows for on-demand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia - over 500,000 articles and 6,000,000 links - with interactive performance on standard consumer-level computer hardware. <s> BIB005
Principle. Counteract fidelity problems in visual aggregates. Discussion. Data abstraction operations simplify data sets, but inherent in this process is also a loss of fidelity-the visualization may, in effect, lie about the size of the effect BIB001 ,  . This should be made clear to viewers using a visualization, either explicitly as done by Cui et al. BIB002 , or by indications in the visual aggregates (i.e., deliberately choosing rough or imprecise visual indications of the aggregated data). The use of averaging for data aggregation can be particularly problematic; Hans Rosling's TED 2006 talk about global health highlights how misleading averages can be when aggregating groups of countries together. Or, as Andrienko and Andrienko remark, "the mean surface temperature on the Moon may seem quite comfortable, but the actual temperature ranges from À230 to þ130 C, and this should be taken into account in designing clothes for astronauts." BIB003 Fig. BIB004 . Visual edge aggregate representations for ZAME BIB005 (average color shade, average, min/max curve, min/max range, min/max tribox, Tukey box, smooth histogram, and step histogram). Fig. 15 . ZAME BIB005 visualizing a protein-protein interaction data set of 100,000 nodes and 1,000,000 edges. Inset shows a magnified view of step histogram edge aggregates in the matrix. Fortunately, the very tool provided by this paper-interactive aggregation control-is exactly what is needed to counteract this problem. Drilling down into the data set, if only a few levels down, will invariably uncover inconsistencies compared to higher-level aggregations.