Visualization

Compound graphs, a frequently encountered type of data set, have a hierarchical tree structure with parent-child relations (‘inclusion’ relations) and non-hierarchical relations between leaf nodes (‘adjacency’ relations). Such datasets are common in software systems, social networks, and citation networks, amongst other scenarios. Visualizing these data sets is difficult because the addition of adjacency relations in any existing tree visualization method results in visual clutter. Moreover, using a generic visualization approach for these yields poor results too because of the difficulty of separating the inclusion and the adjacency relations. The existing methods for visualizing compound graphs and compound directed graphs (hereafter collectively referred to as compound (di)graphs) have numerous shortcomings, such as the inefficient usage of the available space (radial and balloon layout-based tree visualization techniques), the lack of flexibility (methods for drawing clustered graphs), inability to scale well for compound (di)graphs with large hierarchies (ArcTree-based visualization), unintuitive presentation (matrix view based methods), and excessive clutter because of several “extra routing nodes” introduced by binary splits (flow map layouts). Drawing inspiration from the management and routing of electrical and network cables, this paper presents hierarchical edge bundles for visualizing compound (di)graphs. It is a flexible and an intuitive technique that can be used in conjunction with existing tree visualization methods and reduces visual clutter when working with a high count of adjacency relations. ...

Over the last couple of decades, large multi-dimensional databases have become ubiquitous in a vast array of application areas, such as corporate data warehouses as well as projects in scientific computing such as the Human Genome Project and the Digital Sky Survey. One of the major challenges in extracting meaningful information from such large scale databases is the “discover structure, find patterns, and derive causal relationships” from the data. A popular approach is to treat these databases as $n$-dimensional cubes, where each dimension corresponds to a dimension in the relational schema. One of the most popular interfaces for working with multi-dimensional databases is Pivot Table, largely popularized by Microsoft Excel, which allows the aforementioned data cubes to be rotated or pivoted so as to encode its various dimensions as rows or columns of the table. Previous work in this area can broadly be categorized into 3 main areas of focus: (a) formalisms for graphical specifications which include earlier works such as Bertin’s ‘Semiology of Graphics’ as well as recent work such as Wilkinson’s ‘The Grammar of Graphics’, (b) table-based displays which include static table displays such as scatterplot matrices and Tellis displays as well as interactive ones such as Pivot Tables, and (c) tools for visual exploration of datasets, such as VQE, Visage, DEVise, Tioga-2, and VisDB. This paper presents Polaris, a multi-dimensional database exploration interface extending the Pivot Table interface and allowing for direct generation of “rich, expressive set of” graphical displays. Using an algebraic formalism over the database fields, Polaris constructs tables consisting of layers and panes, with the possibility of a different graphics in each pane. For the sake of brevity of this summary, although the paper provides detailed description of the Polaris system, we only discuss its major components here. ...