Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Discriminability (G4) <s> A new method is presented to get an insight into univariate time series data. The problem addressed is how to identify patterns and trends on multiple time scales (days, weeks, seasons) simultaneously. The solution presented is to cluster similar daily data patterns, and to visualize the average patterns as graphs and the corresponding days on a calendar. This presentation provides a quick insight into both standard and exceptional patterns. Furthermore, it is well suited to interactive exploration. Two applications, numbers of employees present and energy consumption, are presented. <s> BIB001 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Discriminability (G4) <s> We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent-child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data. <s> BIB002
Principle. Design visual aggregates to be easily distinguishable from visual data items. Discussion. Visual aggregates and visual data items must be easy to differentiate in order to not convey the wrong information to the viewer. While using the same visual representation for both aggregates as for items is often easiest, it may give the viewer the wrong idea about the data. For example, the aggregate of two points in a scatterplot might be a single point, or the aggregate of two polylines in a parallel coordinate display might be a single polyline on their average BIB001 . A better choice is to introduce a novel representation for the visual aggregate, or to decorate the aggregate with some extra graphical feature, like the shadows for the growing polygons technique BIB002 (Fig. 13) .