Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series
This work addresses the gap in accessing textual information from structured datasets outside search engines for users of visualization tools, offering a novel solution for exploratory data analysis.
The paper tackles the problem of constructing text-based search queries from user interactions with time series visualizations, presenting Q4EDA, a framework that converts visual selections into valid search queries for general-purpose search engines and provides related information suggestions, validated through an application linking Gapminder's line-chart replica with Wikipedia documents to enhance exploratory analysis of UN world indicators.
Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users' visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder's line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations.