Transformation of Data Representation: An Overview of Adaptable Information Display Models
In the realm of data analysis, interactive visualizations have emerged as a powerful tool for exploring complex datasets. These visualizations, which can be manipulated and transformed in real-time, offer a dynamic approach to understanding data, a concept that Riccardo Mazza's "Introduction to Information Visualization" refers to as transformable representations.
One such example of transformable representations is the Attribute Explorer, a tool developed by Bob Spence. This innovative tool uses cursors and histograms to examine a dataset, allowing users to filter data and gain valuable insights.
The magic lens, developed by the Xerox PARC laboratory team, is another transformative tool. It allows users to filter data by placing a lens over a part of an information visualization, providing a simultaneous use of multiple lenses for different filtering operations.
Data filtering, a common practice in software like Excel and Word, is an essential component of these tools. By filtering data, analysts can narrow down data ranges for analysis, eliminating unnecessary data and focusing on what truly matters.
Dynamic querying, a technique that generates queries from user actions, is another key element of transformable representations. Dynamic querying tools often come in the form of graphical interface elements like clickable calendars, radio buttons, and sliders, making it easy for users to interact with the data without needing to understand complex query languages or relational databases.
Riccardo Mazza's book outlines five common techniques for transformable representations: Data Filtering at the Input Stage, Data Reordering, Dynamic Querying, Magic Lenses, and Attribute Exploration. These techniques, when combined, offer a comprehensive approach to creating interactive visualizations that are not static but can morph and respond to user interaction.
Interactive visualizations can be made more engaging through the use of storytelling elements. Annotations and linked narratives can help guide users through the data, providing context and insight.
Data filtering can be particularly beneficial for analysts, as it allows them to compare different data sets or products. By filtering data, analysts can uncover hidden relationships and trends, leading to more informed decision-making.
In conclusion, transformable information representations offer a dynamic and interactive approach to data analysis. By providing tools like the Attribute Explorer and the magic lens, analysts can explore complex datasets in new and exciting ways, gaining deeper insights and making more informed decisions.
[1] Mazza, Riccardo. Introduction to Information Visualization. O'Reilly Media, Inc., 2014. [2] Tweedie, Lisa, Bob Spence, David Williams, and Ravinder Bhogal. "The Attribute Explorer." IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 6, 2011, pp. 1444-1453.
A hero image for this article can be found under the CC BY 2.0 license, credited to pushandplay.
Science and data-and-cloud-computing are key elements in the development and application of technology like interactive visualizations. For instance, the Attribute Explorer, a tool created by Bob Spence, uses science to analyze datasets, allowing users to filter data for valuable insights (Tweedie et al., 2011). Similarly, Dynamic querying, a technique that generates queries from user actions, is a technology driven by data analysis, often implemented through graphical interfaces for user convenience (Mazza, 2014).
