Towards Text Search for Information Visualization
Romain Vuillemot, Mali Akmanalp
We investigate the use of text search to retrieve information visualizations. This is important as the body of available visualizations on the web is growing and they are difficult to find as they don't have immediate textual description. Our approach is first to create a taxonomy of textual terms to describe visualizations, both in a general and in a specific way using economics visualization as application domain. Then we designed and implemented a search engine to query this vocabulary on a real website using economics data. Results from an exploratory study informed us on the types of tasks to support and on the visual design of the search widget, such as showing recommendations similar to Google results. Those early and promising results pave the way for a more diverse and complex vocabulary that exploits the full wealth of information that graphical elements can contain. Such a search feature has wide ranging applicability, from making visualizations more accessible for less technical users, to using other input modalities such as voice.