Transformation of Data Representation: Exploring Adaptable Information Displays

Transformable models of information visualization are gaining traction as a powerful tool for data analysis. These models allow users to easily manipulate data, providing a more interactive and intuitive approach to understanding complex datasets.

One of the key innovations in this field is Dynamic Querying. This technique involves generating queries from user actions to transform an information visualization, without requiring users to understand database query languages. Dynamic Querying tools often include graphical interface elements like clickable calendars, radio buttons, and sliders, making data exploration more accessible.

The concept of Dynamic Querying was first introduced in the 1990s with the development of the Attribute Explorer, a tool created by Bob Spence. The Attribute Explorer displayed data attributes as histograms, and filtering one histogram filtered all others at the same time. This simultaneous filtering feature made it easier for users to identify patterns and relationships within the data.

Another important technique is Data Filtering, which eliminates unnecessary data and narrows down data ranges for analysis. This process allows users to choose which data attributes to examine before visualization, making the analysis more focused and efficient.

Data Reordering, similar to sorting data in Excel or Word, helps to identify potential relationships between attributes. By rearranging the data, users can uncover hidden patterns and trends that might be overlooked in a traditional linear presentation.

The magic lens, developed by the Xerox PARC laboratory team, is a software tool for filtering data in an information visualization. Users can place a lens over a part of a visualization and filter the data that is seen under the lens, providing a more targeted view of the data.

Ronald Coase's quote, "If you torture the data long enough, it will confess," can be applied to transformable information representations. By manipulating the data in various ways, users can uncover insights that might not be immediately apparent.

For those interested in learning more about transformable information visualizations, Riccardo Mazza's book "Introduction to Information Visualization" offers a comprehensive guide. Unfortunately, more information about the original creators of the Attribute Explorer, such as Bob Spence, or where to download the project, is not readily available.

In conclusion, transformable information visualizations are revolutionizing the way we analyse data. By providing users with the ability to manipulate data in real-time, these tools are making complex data more accessible and understandable for everyone.