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Knowing how to construct text-based Search Queries (SQs) for use in SearchEngines (SEs) such as Google or Wikipedia has become a fundamental skill.Though much data are available through such SEs, most structured datasets liveoutside their scope. Visualization tools aid in this limitation, but no suchtools come close to the sheer amount of information available throughgeneral-purpose SEs. To fill this gap, this paper presents Q4EDA, a novelframework that converts users' visual selection queries executed on top of timeseries visual representations, providing valid and stable SQs to be used ingeneral-purpose SEs and suggestions of related information. The usefulness ofQ4EDA is presented and validated by users through an application linking aGapminder's line-chart replica with a SE populated with Wikipedia documents,showing how Q4EDA supports and enhances exploratory analysis of United Nationsworld indicators. Despite some limitations, Q4EDA is unique in its proposal andrepresents a real advance towards providing solutions for querying textualinformation based on user interactions with visual representations.

Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series