Data Visualization Models

Coordinating Multiple Visualizations

• Coordinating multiple visualizations of the same architectural model is crucial to ensure consistency with each other and the model.
• Errors in the model are reflected in the visualizations.
• Simplifying assumptions can make coordination easier, such as:
  • Only one visualization operating at a time.
  • Only one tool operating on the model at a time.

Solutions for Coordinating Multiple Visualizations

• Peer-to-Peer Coordination: Each visualization communicates with each other for updates.
  • Has scaling problems.
  • Works best for visualizations known a priori.
• Push-Based Solution: Visualizations actively notify and update themselves whenever the model changes.
  • Best for multiple simultaneous visualizations.
  • Hard to debug, with potential infinite loops and subtle concurrency conditions.

Guidelines for Constructing New Visualizations

• Borrow elements from similar visualizations to leverage existing stakeholder knowledge and improve comprehensibility.
• Be consistent among visualizations, avoiding conflicts with existing visualizations without a good reason.
• Give meaning to each visual aspect of elements, prioritizing parsimony over aesthetics.
• Document the meaning of visualizations.
• Balance traditional and innovative interfaces, using popular notations (e.g., tables).

Models vs. Visualizations

• A model is abstract information, a set of design decisions.
• Visualizations give design decisions form, allowing depiction and interaction with them in different ways.
• Visualizations are often active, being both pictures and tools.

Canonical Visualizations

• Each modeling notation is associated with one or more canonical visualizations.
• This makes it easy to think of a notation and a visualization as the same thing, even though they are not.

Textual Visualizations

• Generally use an ordinary text editor or word processor.
• Some advanced mechanisms available, such as syntax highlighting, static checking, autocomplete, and structural folding.
• Example: XML visualization.

Graphical Visualizations

• Generally use graphical editors with point-and-click interfaces.
• Employ metaphors like scrolling, zooming, and "drill-down".
• Editors have varying levels of awareness for different target notations.
• More exotic editors and interaction mechanisms exist in research, such as 3D editors and "sketching-based" editors.

Hybrid Visualizations

• Many visualizations are text-only.
• Few graphical notations are purely symbolic.
• Text labels and annotations are generally textual as well.
• Some notations incorporate substantial parts that are mostly graphical alongside substantial parts that are mostly or wholly textual.

Evaluating Visualizations

• Scope and Purpose: Consider what the visualization is for.
• Other factors to evaluate: Depiction, Interaction, Dynamism, View Coordination, Aesthetics, and Extensibility.

Distinguishing between models and visualizations, and understanding how visualizations give form to design decisions and enable interaction. This quiz covers the basics of data visualization and its importance in data analysis.