Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Rendering <s> In many contexts, humans often represent their own “neighborhood” in great detail, yet only major landmarks further away. This suggests that such views (“fisheye views”) might be useful for the computer display of large information structures like programs, data bases, online text, etc. This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation. <s> BIB001 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Rendering <s> We present a novel tree browser that builds on the conventional node link tree diagrams. It adds dynamic rescaling of branches of the tree to best fit the available screen space, optimized camera movement, and the use of preview icons summarizing the topology of the branches that cannot be expanded. In addition, it includes integrated search and filter functions. This paper reflects on the evolution of the design and highlights the principles that emerged from it. A controlled experiment showed benefits for navigation to already previously visited nodes and estimation of overall tree topology. <s> BIB002 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Rendering <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
Rendering a hierarchical aggregated visualization amounts to traversing the visual aggregate hierarchy. The traversal should typically be breadth-first to resolve depth ordering so that higher-level aggregates are correctly occluded by lower (more detailed) levels. This also allows for aborting the rendering at any stage, such as for maintaining a fixed frame rate, while still retaining a consistent visual output. The visualization keeps track of the current detail level it is being viewed at, corresponding to the height in the tree to traverse to while rendering. Depending on the current state of the visualization, there are four main types of rendering traversals (Fig. 2) . . Above traversal: All nodes above (and including) the current height are rendered. Here, data items are abstracted by high-level visual aggregates, hiding details to avoid perceptual and technical overload (Fig. 2a) . . Below traversal: All nodes below (and including) the current height are rendered. Less common, this gives an indication of how data items are grouped together (Fig. 2b ). . Level traversal: All nodes on the same level as the current height are rendered. This gives a snapshot of the data abstraction at any specific detail level (Fig. 2c ). . Range traversal: All nodes in an interval of levels are rendered. As for level traversal, range traversals give a snapshot of the data abstraction at a specific level, as well as some additional detail on the underlying aggregate hierarchy (Fig. 2d ). Beyond this, it is always possible to perform unbalanced traversals of the aggregate tree, e.g., to give details-ondemand to certain branches of the tree without traversing into neighboring branches. In particular, we could let the underlying variability of each aggregate automatically guide the rendering traversal BIB003 . This would devote more detail to the "interesting" parts of a data set, where there is a lot of varying features in the data, and save the entity budget in areas with low variance. Unbalanced rendering traversals can also be used to support generalized fisheye views BIB001 . This is done by letting the budget take a level of detail weight BIB001 , BIB002 associated with the structure into account. Clearly, the benefit of a data aggregate hierarchy and a corresponding visual aggregate hierarchy is that the resulting visualization can be adapted to the requirements of the human user as well as the technical limitations of the visualization platform. Instead of drawing an entire data set as individual data items, a visualization technique supporting hierarchical aggregation can maintain a visual entity budget and only do a breadth-first traversal into the aggregate hierarchy until the budget has been expended. Since any visual aggregate at any level in the tree is guaranteed to serve as a faithful abstraction of underlying levels, the resulting image will remain consistent with the actual data.