Content-Preserving Graphics

Visualization is a common method used to communicate information about underlying data. It requires prudent editorializing and appropriate abstractions to produce a meaningful and compelling result. And, to be most effective, visual designers and analysts should understand the context in which their visual message will be observed, understood, and used. Unfortunately, if the observation context that a visual designer anticipates varies from the actual context in which a visual message is used, many kinds of problems may arise. This situation is especially common in the Web environment, where the audience is distributed and detached from the original author. We propose a Linked Data technique to preserve content within graphics, so that analysts may, when needed, augment the original content in an isolated graphic to satisfy tasks unanticipated by the original visual designer. Our technique uses common web standards to publish, integrate, and access data among disparate but coordinating agencies and enables a new class of knowledge discovery that goes beyond the information in any one visualization to enable the discovery of patterns among the content presented within a corpus of visualizations. By adopting the perspective that graphics are merely derived data subsets, we offer a new consumption method that treats data and visuals uniformly such that sharing the graphic is sharing the data. We offer a prototype implementation of the technique and demonstrate the advantages of this new means for sharing and consuming Linked Data.


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Research Areas: Semantic Web Technology

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