Modern dynamic data visualization environments often feature complex displays comprised of many interactive components, such as plots, axes, and others. These components typically contain attributes or properties that can be manipulated programmatically or interactively. Component property manipulation is usually a two-stage process. The user first selects or in some way identifies the component to be revised and then invokes some other technique or procedure to modify the property of interest. Until recently, components typically have been manipulated one at a time, even if the same property is being modified in each component. How to effectively select multiple components interactively in multiple-view displays remains an open issue. This paper proposes modeling the display components with conventional data sets and reusing simple dynamic graphics, such as a scatter plot or a bar chart, as the graphical user interface to select these elements. This simple approach, called plot of plots, provides a uniform, flexible, and powerful scheme to select multiple display components. In addition, another approach called selection glass is also presented. The selection glass is a tool glass with click-on and click-through selection tool widgets for the selection of components. The availability of the plot of plots and selection glass provides a starting point to investigate new techniques to simultaneously modify the same properties on multiple components.
This paper advocates the study and use of visualization design patterns to improve development productivity and usage effectiveness in dynamic, analytical data visualization. Nine visualization design patterns are presented formally using the current de facto pattern description language. Organized in three categories (data, structural, and behavioral), these patterns summarize many common practices and techniques used in the process of dynamic, analytical data visualization. A relationship diagram is also introduced to illustrate the common relationships and uses of the patterns. Driven by the study of design patterns, a simple, yet powerful, architecture design for a dynamic, analytical data visualization library is proposed. The fundamental characteristics of the design are component-based, data centric, and layout-aided.