Polaris: a system for query, analysis and visualization of multi-dimensional relational databases

Abstract

In the last several years, large multi-dimensional databases have become common in a variety of applications such as data warehousing and scientific computing. Analysis and exploration tasks place significant demands on the interfaces to these databases. Because of the size of the data sets, dense graphical representations are more effective for exploration than spreadsheets and charts. Furthermore, because of the exploratory nature of the analysis, it must be possible for the analysts to change visualizations rapidly as they pursue a cycle involving first hypothesis and then experimentation. In this paper we present Polaris, an interface for exploring large multi-dimensional databases that extends the well-known Pivot Table interface. The novel features of Polaris include an interface for constructing visual specifications of table-based graphical displays and the ability to generate a precise set of relational queries from the visual specifications. The visual specifications can be rapidly and incrementally developed, giving the analyst visual feedback as they construct complex queries and visualizations.

Disperse: Enabling Web-Based Visualization in Multi-screen and Multi-user Environments

“…This is usually accomplished using tabs or a menu of views [6,15,22,33]. However, this strategy prevents users from seeing multiple views concurrently and directly observing how changes in one view affect the others.…”

invis: Exploring high-dimensional RNA sequences from in vitro selection

“…Since these visualizations have limited use as static graphics, many tools for multidimensional visualization (e.g., [3,4,33,35]) also include interaction techniques such as filtering, zooming, and brushing & linking.…”

DaisyViz: A model-based user interface toolkit for interactive information visualization systems

“…Some infovis tools have been developed especially for the visualization of a given data type, such as static graph visualization (e.g., Graphviz [35] and GVF [36]) and multidimensional visualization (e.g., Polaris [14]). These tools cannot cover all seven types of data [6], especially the popular data types of tree, graph, multi-dimensional, and temporal data.…”

“…Based on the task taxonomy by Shneiderman [6] and new task requirements seen in recent toolkits [7][8][9][10][11][12][13][14][15][16][17][18], we developed a more detailed taxonomy for infovis tasks, which includes overview of multiple views, overview of visualization in a view, pan, zoom, filter by data attributes (i.e., dynamic query), filter by legends, keyword search, detail tooltip (i.e., detail-on-demand), and coordination (e.g., brushingand-linking and drill-down). For the tasks of coordination, brushing-and-linking refers to a technique in which when an item is selected in one view highlights, the corresponding item(s) in another view will be highlighted; drilldown is a tool that loads related items in a view when they are selected in another [11].…”

“…One of the challenges is to provide a balance between the flexibility required to integrate domain-specific models into visualization tools and the simplicity needed to build and access visualization tools. Despite the challenges various infovis toolkits, such as PAD++ [8], Jazz [9], Piccolo [10], Snap-Together [11], GeoVista [12], Improvise [41], PRISMA [13], Polaris [14], XML toolkit [15], Fekete's infovis toolkit [16], prefuse [7], Many Eyes [17], and ComVis [18], have been developed. Most of the toolkits support the rapid prototype of visualization systems, the addition of domain knowledge, and domain-specific task models.…”